CN114445769A - Fishing behavior detection method, device and system - Google Patents
Fishing behavior detection method, device and system Download PDFInfo
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- CN114445769A CN114445769A CN202111655376.2A CN202111655376A CN114445769A CN 114445769 A CN114445769 A CN 114445769A CN 202111655376 A CN202111655376 A CN 202111655376A CN 114445769 A CN114445769 A CN 114445769A
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Abstract
The application provides a fishing behavior detection method, a fishing behavior detection device and a fishing behavior detection system, which are used for solving the problems that the method for detecting a fishing rod and a person in the existing fishing behavior detection method is low in accuracy and robustness and high in false alarm rate of a mode for determining a fishing behavior, and the method comprises the following steps: acquiring an image to be detected from a video acquired aiming at a fishing forbidden region; performing target detection on an image to be detected based on a target detection model to obtain a detected image, and determining whether a first fishing rod target set and a first human body target set exist in the detected image; if the first set of fishing rod objects and the first set of human objects exist, it is determined whether a set of fishing behavior objects exists in the detected image.
Description
Technical Field
The present application relates to the field of image processing technologies, and in particular, to a method, an apparatus, and a system for detecting a fishing behavior.
Background
Fishing is a popular leisure mode, but some regions are required to be fishing-prohibited due to safety considerations or management, such as lakes in scenic spots, private-contracted reservoirs, etc., and the fishing-prohibited regions are generally provided with warning boards or arranged to be patrolled by managers in order to drive fishermen.
At present, as the driving effect of the warning board is not obvious, management personnel are arranged to patrol and waste manpower and material resources, so that a fishing forbidden area is monitored in real time, when a fishing rod and people are detected in a monitoring picture, warning voice is played, and the monitoring picture of the detected fishing rod and people is sent to the management personnel, so that the driving effect is enhanced, and the manpower and material resources are saved. However, the traditional image processing method is adopted to detect the fishing rod and the person, the accuracy is low, the robustness is weak, the fishing rod and the person are only detected to determine the existence of fishing behaviors, and the false alarm rate is high.
Therefore, the existing fishing behavior detection method has the problems that the method for detecting the fishing rod and the person is low in accuracy and robustness, and the false alarm rate of the mode for determining the fishing behavior is high.
Disclosure of Invention
The embodiment of the application provides a fishing behavior detection method, device and system, and is used for solving the problems that the method for detecting a fishing rod and a person in the existing fishing behavior detection method is low in accuracy and robustness, and the misstatement rate of the mode for determining the fishing behavior is high.
In a first aspect, to solve the above technical problem, an embodiment of the present application provides a fishing behavior detection method, including:
acquiring an image to be detected;
performing target detection on the image to be detected based on a target detection model to obtain a detected image, and determining whether a first fishing rod target set and a first human body target set exist in the detected image; the position parameters of the detection frame in the detected image comprise the coordinate, the width, the height and the inclination angle of a central point, and the inclination angle is an included angle between the detection frame and a horizontal axis of the detected image;
if a first fishing rod target set and a first human body target set exist, determining whether a fishing behavior target set exists in the detected image; wherein the set of fishing performance goals includes any fishing rod goal in the first set of fishing rod goals and any human goal in the associated first set of human goals.
In the embodiment of the application, an image to be detected can be obtained, target detection is performed on the image to be detected based on a target detection model, a detected image is obtained, and whether a first fishing rod target set and a first human body target set exist in the detected image or not is determined, wherein position parameters of a detection frame in the detected image include coordinates, width, height and an inclination angle of a central point, the inclination angle is an included angle between the detection frame and a horizontal axis of the detected image, and if the first fishing rod target set and the first human body target set exist, whether a fishing behavior target set exists in the detected image or not is determined, wherein the fishing behavior target set includes any fishing rod target in the first fishing rod target set and any human body target in the associated first human body target set. The positions of the detection frames of the fishing rod target and the human body target in the detected image are indicated through position parameters such as coordinates, width, height, inclination angle and the like of the central point, the accuracy and robustness of detection results of the fishing rod target and the human body target are improved, whether the fishing rod target and the human body target existing in the detected image are related or not is determined, and the false alarm rate of fishing behaviors in a fishing forbidden area is reduced.
In an optional implementation manner, before performing target detection on the image to be detected based on a target detection model to obtain a detected image, the method further includes:
acquiring at least one sample image; the position parameters of the real frames of the fishing rod target and the human body target in the sample image comprise the coordinate, the width, the height and the inclination angle of a central point, and the inclination angle is an included angle between the real frame and a horizontal axis of the sample image;
training a preset detection model based on the at least one sample image to obtain the target detection model; the target detection model is used for detecting a fishing rod target and a human body target in the image.
In an alternative embodiment, determining whether a set of phishing behavior targets exists in the detected image comprises:
deleting the fishing rod targets with the confidence coefficient smaller than a first preset threshold value in the first fishing rod target set and the human body targets with the confidence coefficient smaller than a second preset threshold value in the first human body target set to obtain a second fishing rod target set and a second human body target set;
determining the intersection and combination ratio of the detection frame of any fishing rod target in the second fishing rod target set and the detection frame of any human body target in the second human body target set;
if the intersection comparison is larger than a third preset threshold value, associating the corresponding fishing rod target and the human body target, and determining that the fishing behavior target set exists in the detected image;
if the intersection comparison is not larger than the third preset threshold, determining the distance between the corresponding fishing rod target and the human body target; wherein the distance is the minimum Euclidean distance between each vertex of the detection frame of the fishing rod target and each edge of the detection frame of the human body target;
and if the distance is not greater than a fourth preset threshold value, associating the intersection and comparing the corresponding fishing rod target and the human body target, and determining that the fishing behavior target set exists in the detected image.
In an optional embodiment, determining an intersection ratio of a detection frame of any one of the second set of fishing rod objects and a detection frame of any one of the second set of human targets comprises:
acquiring position parameters of a detection frame of any one fishing rod target in the second fishing rod target set and position parameters of a detection frame of any one human target in the second human target set;
determining coordinates of an intersection point of the detection frame of any fishing rod target and the detection frame of any human body target based on the position parameters of the detection frame of any fishing rod target and the position parameters of the detection frame of any human body target;
and determining the intersection ratio of the detection frame of any fishing rod target and the detection frame of any human body target based on the coordinates of the intersection points.
In an alternative embodiment, determining the intersection ratio of the detection frame of any fishing rod target and the detection frame of any human target based on the coordinates of the intersection point comprises:
determining the intersection ratio of the detection frame of any fishing rod target and the detection frame of any human body target by adopting a preset formula based on the coordinates of the intersection points;
the preset formula specifically includes:
wherein the IOU is the intersection ratio of the detection frame of any fishing rod target and the detection frame of any human body target,
the area of the detection frame of any fishing rod target,
the area of the detection frame of any human target (m)i,ni) Is the coordinate of the ith intersection point, and n is the number of the intersection points.
In an alternative embodiment, the method further comprises:
if the first human body target set does not exist or the phishing behavior target set does not exist, the detected image is sent to a manager;
and if the fishing behavior target set exists, sending the detected image to the manager, and playing warning voice in a fishing-forbidden area.
In a second aspect, an embodiment of the present application further provides a fishing behavior detection device, including:
the acquisition module is used for acquiring an image to be detected;
the detection module is used for carrying out target detection on the image to be detected based on a target detection model to obtain a detected image and determining whether a first fishing rod target set and a first human body target set exist in the detected image or not; the position parameters of the detection frame in the detected image comprise the coordinate, the width, the height and the inclination angle of a central point, and the inclination angle is an included angle between the detection frame and a horizontal axis of the detected image;
a determining module, configured to determine whether a fishing behavior target set exists in the detected image if a first fishing rod target set and a first human body target set exist; wherein the set of fishing performance goals includes any fishing rod goal in the first set of fishing rod goals and any human goal in the associated first set of human goals.
In an alternative embodiment, the apparatus further comprises a training module for:
acquiring at least one sample image; the position parameters of the real frames of the fishing rod target and the human body target in the sample image comprise the coordinate, the width, the height and the inclination angle of a central point, and the inclination angle is an included angle between the real frame and a horizontal axis of the sample image;
training a preset detection model based on the at least one sample image to obtain the target detection model; the target detection model is used for detecting a fishing rod target and a human body target in the image.
In an optional implementation manner, the determining module is specifically configured to:
deleting the fishing rod targets with the confidence coefficient smaller than a first preset threshold value in the first fishing rod target set and the human body targets with the confidence coefficient smaller than a second preset threshold value in the first human body target set to obtain a second fishing rod target set and a second human body target set;
determining the intersection and combination ratio of the detection frame of any fishing rod target in the second fishing rod target set and the detection frame of any human body target in the second human body target set;
if the intersection comparison is larger than a third preset threshold value, associating the corresponding fishing rod target and the human body target, and determining that the fishing behavior target set exists in the detected image;
if the intersection comparison is not larger than the third preset threshold, determining the distance between the corresponding fishing rod target and the human body target; wherein the distance is the minimum Euclidean distance between each vertex of the detection frame of the fishing rod target and each edge of the detection frame of the human body target;
and if the distance is not greater than a fourth preset threshold value, associating the intersection and comparing the corresponding fishing rod target and the human body target, and determining that the fishing behavior target set exists in the detected image.
In an optional implementation manner, the determining module is specifically configured to:
acquiring position parameters of a detection frame of any one fishing rod target in the second fishing rod target set and position parameters of a detection frame of any one human target in the second human target set;
determining coordinates of an intersection point of the detection frame of any fishing rod target and the detection frame of any human body target based on the position parameters of the detection frame of any fishing rod target and the position parameters of the detection frame of any human body target;
and determining the intersection ratio of the detection frame of any fishing rod target and the detection frame of any human body target based on the coordinates of the intersection points.
In an optional implementation manner, the determining module is specifically configured to:
determining the intersection ratio of the detection frame of any fishing rod target and the detection frame of any human body target by adopting a preset formula based on the coordinates of the intersection points;
the preset formula specifically includes:
wherein the IOU is the intersection ratio of the detection frame of any fishing rod target and the detection frame of any human body target,
the area of the detection frame of any fishing rod target,
the area of the detection frame of any human target (m)i,ni) Is the coordinate of the ith intersection point, and n is the number of the intersection points.
In an optional embodiment, the apparatus further comprises a processing module configured to:
if the first human body target set does not exist or the phishing behavior target set does not exist, the detected image is sent to a manager;
and if the fishing behavior target set exists, sending the detected image to the manager, and playing warning voice in a fishing-forbidden area.
In a third aspect, an embodiment of the present application further provides a fishing behavior detection system, including:
a memory for storing program instructions;
and the processor is used for calling the program instructions stored in the memory and executing the steps included in any one of the implementation modes of the first aspect according to the obtained program instructions.
In a fourth aspect, embodiments of the present application provide a storage medium storing computer-executable instructions for causing a computer to perform the steps included in any one of the embodiments of the first aspect.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application.
Fig. 1 is a schematic structural diagram of a fishing behavior detection system according to an embodiment of the present disclosure;
fig. 2a is a schematic flow chart of a fishing behavior detection method according to an embodiment of the present disclosure;
FIG. 2b is a schematic diagram of a detected image according to an embodiment of the present disclosure;
FIG. 2c is a schematic diagram of an intersection point of a detection frame of a fishing rod target and a detection frame of a human body target according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a fishing behavior detection device according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of another fishing behavior detection system according to an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application. In the present application, the embodiments and features of the embodiments may be arbitrarily combined with each other without conflict. Also, while a logical order is shown in the flow diagrams, in some cases, the steps shown or described can be performed in an order different than here.
The terms "first" and "second" in the description and claims of the present application and the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the term "comprises" and any variations thereof, which are intended to cover non-exclusive protection. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
In the embodiments of the present application, "at least one" may mean at least two, for example, two, three, or more, and the embodiments of the present application are not limited.
In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" in this document generally indicates that the preceding and following related objects are in an "or" relationship unless otherwise specified.
At present, as the driving effect of the warning board is not obvious, management personnel are arranged to patrol and waste manpower and material resources, so that a fishing forbidden area is monitored in real time, when a fishing rod and people are detected in a monitoring picture, warning voice is played, and the monitoring picture of the detected fishing rod and people is sent to the management personnel, so that the driving effect is enhanced, and the manpower and material resources are saved. However, the traditional image processing method is adopted to detect the fishing rod and the person, the accuracy is low, the robustness is weak, the fishing rod and the person are only detected to determine the existence of fishing behaviors, and the false alarm rate is high. Therefore, the existing fishing behavior detection method has the problems that the method for detecting the fishing rod and the person is low in accuracy and robustness, and the false alarm rate of the mode for determining the fishing behavior is high.
In view of this, an embodiment of the present application provides a fishing behavior detection method, which may obtain an image to be detected, perform target detection on the image to be detected based on a target detection model to obtain a detected image, and determine whether a first fishing rod target set and a first human body target set exist in the detected image, where a position parameter of a detection frame in the detected image includes a coordinate of a central point, a width, a height, and an inclination angle, the inclination angle is an included angle between the detection frame and a horizontal axis of the detected image, and if the first fishing rod target set and the first human body target set exist, determine whether a fishing behavior target set exists in the detected image, where the fishing behavior target set includes any fishing rod target in the first fishing rod target set and any human body target in the associated first human body target set. The positions of the detection frames of the fishing rod target and the human body target in the detected image are indicated through position parameters such as coordinates, width, height, inclination angle and the like of the central point, the accuracy and robustness of detection results of the fishing rod target and the human body target are improved, whether the fishing rod target and the human body target existing in the detected image are related or not is determined, and the false alarm rate of fishing behaviors in a fishing forbidden area is reduced.
In order to better understand the technical solutions, the technical solutions of the present application are described in detail below through the drawings and the specific embodiments of the specification, and it should be understood that the specific features of the embodiments and examples of the present application are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application, and the technical features of the embodiments and examples of the present application may be combined with each other without conflict.
As shown in fig. 1, a schematic structural diagram of a phishing behavior detection system provided in the embodiment of the present application is shown, and it should be understood that the method provided in the embodiment of the present application can be applied to various phishing behavior detection systems, and the phishing behavior detection system shown in fig. 1 is a simple illustration of a phishing behavior detection system to which the method provided in the embodiment of the present application can be applied, and is not a limitation of a phishing behavior detection system to which the method provided in the embodiment of the present application can be applied.
The fishing behavior detection system shown in fig. 1 includes a memory 101, a processor 102, and a bus interface 103. The memory 101 and the processor 102 are connected via a bus interface 103. The memory 101 is used to store program instructions. The processor 102 is configured to call the program instructions stored in the memory 101, and execute all steps included in the fishing behavior detection method according to the obtained program instructions.
It should be noted that, in the embodiment of the present application, the fishing behavior detection system may be installed in an intelligent camera, and the intelligent camera is mainly used for monitoring a lake in a scenic spot, a reservoir for private contract, and other fishing prohibited areas.
Fig. 2a is a schematic flow chart of a phishing behavior detection method provided in an embodiment of the present application, which can be executed by the phishing behavior detection system shown in fig. 1. The specific flow of the method is described below.
Step 201: and acquiring an image to be detected.
In this embodiment of the application, the fishing behavior detection system may obtain an image to be detected from a video acquired by the intelligent camera for the no-fishing region, for example, first decode a video code stream acquired by the intelligent camera for the no-fishing region into an image code stream, then perform sampling processing on the image code stream based on a preset time interval to obtain an image code stream after the sampling processing, then perform format conversion processing on the image code stream after the sampling processing because an image in the YUV format is smaller than an image storage space in the RGB format to obtain an image code stream after the format conversion processing, and obtain the image to be detected from the image code stream after the format conversion processing.
Step 202: and carrying out target detection on the image to be detected based on the target detection model to obtain a detected image, and determining whether a first fishing rod target set and a first human body target set exist in the detected image.
In the embodiment of the application, after the image to be detected is acquired from the video acquired by the intelligent camera for the no-fishing area, the fishing behavior detection system can perform target detection on the image to be detected based on a target detection model to obtain a detected image, and determine whether a first fishing rod target set and a first human body target set exist in the detected image, wherein position parameters of a detection frame in the detected image comprise coordinates, width, height and an inclination angle of a central point, and the inclination angle is an included angle between the detection frame and a horizontal axis of the detected image;
for example, as shown in fig. 2b, for a schematic diagram of a detected image provided in an embodiment of the present application, the position parameters of the detection frames of the human target and the fishing rod target in fig. 2b are both (C)x,CyW, h, θ), wherein (C)x,Cy) The coordinate of the center point of the detection frame, w the width of the detection frame, h the height of the detection frame, and θ the inclination angle of the detection frame (i.e., the included angle between the detection frame and the horizontal axis of the image) may be fixed to 0, since the detection frame of the human target is generally parallel to the horizontal axis. In addition, the position parameters of the detection frame can be represented by (C)x,CyW, h, θ) to (X0,Y0；X1,Y1；X2,Y2；X3,Y3) Wherein (X)0,Y0)，(X1,Y1)，(X2,Y2)，(X3,Y3) For detecting coordinates of the vertices of the frame in sequential order (anticlockwise or clockwise), e.g. (X)0,Y0) To detect the coordinates of the upper left corner of the box, (X)1,Y1) To detect the coordinates of the upper right corner of the box, (X)2,Y2) To detect the coordinates of the lower right corner of the box, (X)3,Y3) The coordinates of the lower left corner of the detection box.
It should be noted that, in this embodiment of the application, before performing target detection on an image to be detected based on a target detection model to obtain a detected image, at least one sample image may be obtained, where position parameters of real frames of a fishing rod target and a human body target in the sample image include coordinates, a width, a height, and an inclination angle of a central point, the inclination angle is an included angle between a horizontal axis of the real frame and a horizontal axis of the image, and a preset detection model is trained based on the at least one sample image to obtain a target detection model for detecting the fishing rod target and the human body target in the image. The preset detection model is a detection model based on a Convolutional Neural Network, such as a Region Convolutional Neural Network (R-CNN), a yolo (young Only Look one), a Single Shot multi box Detector (SSD), and the like. The regression parameters of the preset detection model include coordinates (C) of the center point of the detection framex,Cy) The width w of the detection frame, the height h of the detection frame and the inclination angle theta of the detection frame, and the regression formula of the inclination angle theta of the detection frame is
Wherein, tθTo detect the correction value of the inclination angle theta of the frame,
as the angle of inclination theta of the real frame*Correction value of, thetaaIs the tilt angle of the prior frame. Presetting a regression loss function of the detection model, specifically:
wherein L isrFor regression losses, f (-) is a logarithmic or square root function, m1As coordinates of the center point of the detection frame, m2Is the coordinate of the center point of the real frame.
Step 203: if the first fishing rod target set and the first human body target set exist, whether a fishing behavior target set exists in the detected image or not is determined.
In the embodiment of the application, after the target detection is performed on an image to be detected based on a target detection model to obtain a detected image, and whether a first fishing rod target set and a first human body target set exist in the detected image is determined, if the fishing behavior detection system determines that the first fishing rod target set and the first human body target set exist in the detected image, whether a fishing behavior target set exists in the detected image is determined, wherein the fishing behavior target set comprises any fishing rod target in the first fishing rod target set and any human body target in the associated first human body target set.
Specifically, a fishing rod target with the confidence coefficient smaller than a first preset threshold in a first fishing rod target set and a human body target with the confidence coefficient smaller than a second preset threshold in a first human body target set are deleted to obtain a second fishing rod target set and a second human body target set, the intersection and comparison between the detection frame of any fishing rod target in the second fishing rod target set and the detection frame of any human body target in the second human body target set is determined, if the intersection and comparison is larger than a third preset threshold, the corresponding fishing rod target and the human body target are associated and compared, the fishing behavior target set existing in the detected image is determined, if the intersection and comparison is not larger than the third preset threshold, the distance between the corresponding fishing rod target and the human body target is determined, wherein the distance is the minimum Euclidean distance between each vertex of the detection frame of the fishing rod target and each side of the detection frame of the human body target, and if the distance is not larger than a fourth preset threshold, and then, correlating and comparing the corresponding fishing rod target and the human body target, and determining that a fishing behavior target set exists in the detected image.
It should be noted that, in the implementation of the present application, when determining the intersection and comparison between the detection frame of any one of the fishing rod targets in the second fishing rod target set and the detection frame of any one of the human body targets in the second human body target set, the position parameter of the detection frame of any one of the fishing rod targets in the second fishing rod target set and the position parameter of the detection frame of any one of the human body targets in the second human body target set may be obtained, the coordinates of the intersection point of the detection frame of any one fishing rod target and the detection frame of any one human body target are determined based on the position parameter of the detection frame of any one fishing rod target and the position parameter of the detection frame of any one human body target, and the intersection and comparison between the detection frame of any one fishing rod target and the detection frame of any one human body target is determined by using a preset formula, where the preset formula specifically is:
wherein the IOU is the intersection ratio of the detection frame of any fishing rod target and the detection frame of any human body target,
the area of the detection frame of any fishing rod target,
area of the detection frame for any human target (m)i,ni) Is the coordinate of the ith intersection point, and n is the number of intersection points.
For example, as shown in fig. 2C, for the schematic diagram of the intersection point of the detection frame of any fishing rod target and the detection frame of any human target provided in the embodiment of the present application, the position parameter of the detection frame of any fishing rod target and the position parameter of the detection frame of any human target are represented by (C)x,CyW, h, theta) to (X)0,Y0；X1,Y1；X2,Y2；X3,Y3) Obtaining a detection frame B of any fishing rod target1(X0,Y0；X1,Y1；X2,Y2；X3,Y3) Detection frame B for any human body target2(X0,Y0；X1,Y1；X2,Y2；X3,Y3) Determining that one straight line and two intersected straight lines have intersection points necessarily based on the two points, and determining the coordinate of the intersection point of the position parameter of the detection frame of any fishing rod target and the detection frame of any human body target as (m)1,n1)，(m2,n2)，(m3,n3)，(m4,n4)，(m5,n5)，(m6,n6)，(m7,n7)，(m8,n8)。
After the target detection is carried out on the image to be detected based on the target detection model to obtain a detected image and whether a first fishing rod target set and a first human body target set exist in the detected image or not is determined, if the fishing behavior detection system determines that the first fishing rod target set does not exist in the detected image or the first fishing rod target set and the first human body target set exist in the detected image but the fishing behavior target set does not exist, the detected image is only sent to a manager, and the warning voice is not played in the fishing forbidden area.
After the target detection is carried out on the image to be detected based on the target detection model to obtain a detected image and whether a first fishing rod target set and a first human body target set exist in the detected image or not is determined, if the fishing behavior detection system determines that the first fishing rod target set and the first human body target set exist in the detected image and the fishing behavior target set exists in the detected image, the detected image is sent to a manager, and warning voice is played in a fishing forbidden area.
According to the scheme, the fishing behavior detection system indicates the positions of the fishing rod targets and the detection frames of the human body targets in the detected images through the position parameters such as the coordinates, the width, the height and the inclination angle of the central point, so that the accuracy and the robustness of the detection results of the fishing rod targets and the human body targets are improved, when the fishing rod targets and the human body targets which are associated exist in the detected images are determined, the detected images are sent to a manager, the warning voice is played in the fishing-forbidden area, when only the fishing rod targets exist in the detected images are determined, only the detected images are sent to the manager, the warning voice is not played in the fishing-forbidden area, and the false alarm rate of the fishing behaviors in the fishing-forbidden area is reduced.
Based on the same inventive concept, the embodiment of the present application further provides a fishing behavior detection device, which can be applied to the fishing behavior detection system shown in fig. 1. The phishing behavior detection device can realize the corresponding function of the phishing behavior detection method. The phishing behavior detection means may be a hardware structure, a software module, or a hardware structure plus a software module. The fishing behavior detection device can be realized by a chip system, and the chip system can be formed by a chip and can also comprise the chip and other discrete devices. Referring to fig. 3, a schematic structural diagram of a phishing behavior detection device provided in the present embodiment of the application is shown, where the phishing behavior detection device includes an obtaining module 301, a detecting module 302, a determining module 303, a first processing module 304, and a second processing module 305.
An obtaining module 301, configured to obtain an image to be detected;
a detection module 302, configured to perform target detection on the image to be detected based on a target detection model to obtain a detected image, and determine whether a first fishing rod target set and a first human body target set exist in the detected image; the position parameters of the detection frame in the detected image comprise the coordinate, the width, the height and the inclination angle of a central point, and the inclination angle is an included angle between the detection frame and a horizontal axis of the detected image;
a determining module 303, configured to determine whether a fishing behavior target set exists in the detected image if a first fishing rod target set and a first human body target set exist; wherein the set of fishing performance goals includes any fishing rod goal in the first set of fishing rod goals and any human goal in the associated first set of human goals.
In an alternative embodiment, the apparatus further comprises a training module for:
acquiring at least one sample image; the position parameters of the real frames of the fishing rod target and the human body target in the sample image comprise the coordinate, the width, the height and the inclination angle of a central point, and the inclination angle is an included angle between the real frame and a horizontal axis of the sample image;
training a preset detection model based on the at least one sample image to obtain the target detection model; the target detection model is used for detecting a fishing rod target and a human body target in the image.
In an optional implementation manner, the determining module 303 is specifically configured to:
deleting the fishing rod targets with the confidence coefficient smaller than a first preset threshold value in the first fishing rod target set and the human body targets with the confidence coefficient smaller than a second preset threshold value in the first human body target set to obtain a second fishing rod target set and a second human body target set;
determining the intersection and combination ratio of the detection frame of any fishing rod target in the second fishing rod target set and the detection frame of any human body target in the second human body target set;
if the intersection comparison is larger than a third preset threshold value, associating the corresponding fishing rod target and the human body target, and determining that the fishing behavior target set exists in the detected image;
if the intersection comparison is not larger than the third preset threshold, determining the distance between the corresponding fishing rod target and the human body target; wherein the distance is the minimum Euclidean distance between each vertex of the detection frame of the fishing rod target and each edge of the detection frame of the human body target;
and if the distance is not greater than a fourth preset threshold value, associating the intersection and comparing the corresponding fishing rod target and the human body target, and determining that the fishing behavior target set exists in the detected image.
In an optional implementation manner, the determining module 303 is specifically configured to:
acquiring position parameters of a detection frame of any one fishing rod target in the second fishing rod target set and position parameters of a detection frame of any one human target in the second human target set;
determining coordinates of an intersection point of the detection frame of any fishing rod target and the detection frame of any human body target based on the position parameters of the detection frame of any fishing rod target and the position parameters of the detection frame of any human body target;
and determining the intersection ratio of the detection frame of any fishing rod target and the detection frame of any human body target based on the coordinates of the intersection points.
In an optional implementation manner, the determining module 303 is specifically configured to:
determining the intersection ratio of the detection frame of any fishing rod target and the detection frame of any human body target by adopting a preset formula based on the coordinates of the intersection points;
the preset formula specifically includes:
wherein the IOU is the intersection ratio of the detection frame of any fishing rod target and the detection frame of any human body target,
the area of the detection frame of any fishing rod target,
the area of the detection frame of any human target (m)i,ni) Is the coordinate of the ith intersection point, and n is the number of the intersection points.
In an optional embodiment, the apparatus further comprises a processing module configured to:
if the first human body target set does not exist or the phishing behavior target set does not exist, the detected image is sent to a manager;
and if the fishing behavior target set exists, sending the detected image to the manager, and playing warning voice in a fishing-forbidden area.
Based on the same inventive concept, an embodiment of the present application further provides a fishing behavior detection system, please refer to fig. 4, which is a schematic structural diagram of a fishing behavior detection system provided in the embodiment of the present application, the fishing behavior detection system includes at least one processor 402 and a memory 401 connected to the at least one processor, a specific connection medium between the processor 402 and the memory 401 is not limited in the embodiment of the present application, fig. 4 is an example in which the processor 402 and the memory 401 are connected by a bus 400, the bus 400 is represented by a thick line in fig. 4, and a connection manner between other components is only schematically illustrated and not limited. The bus 400 may be divided into an address bus, a data bus, a control bus, etc., and is shown with only one thick line in fig. 4 for ease of illustration, but does not represent only one bus or type of bus.
In the embodiment of the present application, the memory 401 stores instructions executable by the at least one processor 402, and the at least one processor 402 may perform the steps included in the foregoing phishing behavior detection method by calling the instructions stored in the memory 401. The processor 402 is a control center of the phishing behavior detection system, and can be connected to various parts of the whole phishing behavior detection system by using various interfaces and lines, and by executing instructions stored in the memory 401, various functions of the phishing behavior detection system are realized. Optionally, the processor 402 may include one or more processing units, and the processor 402 may integrate an application processor and a modem processor, wherein the application processor mainly handles operating systems, user interfaces, application programs, and the like, and the modem processor mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 402. In some embodiments, processor 402 and memory 401 may be implemented on the same chip, or in some embodiments, they may be implemented separately on separate chips.
In the embodiments of the present application, the processor 402 may be a general-purpose processor, such as a Central Processing Unit (CPU), a digital signal processor, an application specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or discrete hardware components, and may implement or perform the methods, steps, and logic blocks disclosed in the embodiments of the present application. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the fishing behavior detection method disclosed in the embodiments of the present application may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor.
By programming the processor 402, the code corresponding to the phishing behavior detection method described in the foregoing embodiment may be solidified into a chip, so that the chip can execute the steps of the phishing behavior detection method when running, and how to program the processor 402 is a technique known by those skilled in the art, and will not be described herein again.
Based on the same inventive concept, embodiments of the present application further provide a storage medium storing computer instructions, which, when executed on a computer, cause the computer to perform the steps of the fishing behavior detection method as described above.
In some possible embodiments, the various aspects of the phishing behavior detection method provided herein may also be implemented in the form of a program product comprising program code for causing a phishing behavior detection system to perform the steps of the phishing behavior detection method according to various exemplary embodiments of the present application described above in this specification when the program product is run on the phishing behavior detection system.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. A fishing behavior detection method, comprising:
acquiring an image to be detected;
performing target detection on the image to be detected based on a target detection model to obtain a detected image, and determining whether a first fishing rod target set and a first human body target set exist in the detected image; the position parameters of the detection frame in the detected image comprise the coordinate, the width, the height and the inclination angle of a central point, and the inclination angle is an included angle between the detection frame and a horizontal axis of the detected image;
if a first fishing rod target set and a first human body target set exist, determining whether a fishing behavior target set exists in the detected image; wherein the set of fishing performance goals includes any fishing rod goal in the first set of fishing rod goals and any human goal in the associated first set of human goals.
2. The method of claim 1, wherein before performing the target detection on the image to be detected based on the target detection model to obtain the detected image, the method further comprises:
acquiring at least one sample image; the position parameters of the real frames of the fishing rod target and the human body target in the sample image comprise the coordinate, the width, the height and the inclination angle of a central point, and the inclination angle is an included angle between the real frame and a horizontal axis of the sample image;
training a preset detection model based on the at least one sample image to obtain the target detection model; the target detection model is used for detecting a fishing rod target and a human body target in the image.
3. The method of claim 1 or 2, wherein determining whether a set of phishing behavior objects is present in the detected image comprises:
deleting the fishing rod targets with the confidence coefficient smaller than a first preset threshold value in the first fishing rod target set and the human body targets with the confidence coefficient smaller than a second preset threshold value in the first human body target set to obtain a second fishing rod target set and a second human body target set;
determining the intersection and combination ratio of the detection frame of any fishing rod target in the second fishing rod target set and the detection frame of any human body target in the second human body target set;
if the intersection comparison is larger than a third preset threshold value, associating the corresponding fishing rod target and the human body target, and determining that the fishing behavior target set exists in the detected image;
if the intersection comparison is not larger than the third preset threshold, determining the distance between the corresponding fishing rod target and the human body target; wherein the distance is the minimum Euclidean distance between each vertex of the detection frame of the fishing rod target and each edge of the detection frame of the human body target;
and if the distance is not greater than a fourth preset threshold value, associating the intersection and comparing the corresponding fishing rod target and the human body target, and determining that the fishing behavior target set exists in the detected image.
4. The method of claim 3, wherein determining an intersection ratio of a detection frame of any one of the second set of fishing rod targets and a detection frame of any one of the second set of human targets comprises:
acquiring position parameters of a detection frame of any one fishing rod target in the second fishing rod target set and position parameters of a detection frame of any one human target in the second human target set;
determining coordinates of intersection points of the detection frame of any fishing rod target and the detection frame of any human body target based on the position parameters of the detection frame of any fishing rod target and the position parameters of the detection frame of any human body target;
and determining the intersection ratio of the detection frame of any fishing rod target and the detection frame of any human body target based on the coordinates of the intersection points.
5. The method of claim 4, wherein determining an intersection ratio of the detection frame of the any fishing rod target and the detection frame of the any human target based on coordinates of the intersection point comprises:
determining the intersection ratio of the detection frame of any fishing rod target and the detection frame of any human body target by adopting a preset formula based on the coordinates of the intersection points;
the preset formula specifically includes:
wherein the IOU is the intersection ratio of the detection frame of any fishing rod target and the detection frame of any human body target,
the area of the detection frame of any fishing rod target,
the area of the detection frame of any human target (m)i,ni) Is the coordinate of the ith intersection point, and n is the number of the intersection points.
6. The method of claim 1, further comprising:
if the first human body target set does not exist or the phishing behavior target set does not exist, the detected image is sent to a manager;
and if the fishing behavior target set exists, sending the detected image to the manager, and playing warning voice in a fishing-forbidden area.
7. A fishing behavior detection device, comprising:
the acquisition module is used for acquiring an image to be detected;
the detection module is used for carrying out target detection on the image to be detected based on a target detection model to obtain a detected image and determining whether a first fishing rod target set and a first human body target set exist in the detected image or not; the position parameters of the detection frame in the detected image comprise the coordinate, the width, the height and the inclination angle of a central point, and the inclination angle is an included angle between the detection frame and a horizontal axis of the detected image;
a determining module, configured to determine whether a fishing behavior target set exists in the detected image if a first fishing rod target set and a first human body target set exist; wherein the set of fishing performance goals includes any fishing rod goal in the first set of fishing rod goals and any human goal in the associated first set of human goals.
8. The apparatus of claim 7, wherein the apparatus further comprises a training module to:
acquiring at least one sample image; the position parameters of the real frames of the fishing rod target and the human body target in the sample image comprise the coordinate, the width, the height and the inclination angle of a central point, and the inclination angle is an included angle between the real frame and a horizontal axis of the sample image;
training a preset detection model based on the at least one sample image to obtain the target detection model; the target detection model is used for detecting a fishing rod target and a human body target in the image.
9. A fishing behavior detection system, comprising:
a memory for storing program instructions;
a processor for calling program instructions stored in said memory and for executing the steps comprised by the method of any one of claims 1 to 6 in accordance with the obtained program instructions.
10. A storage medium storing computer-executable instructions for causing a computer to perform the steps comprising the method of any one of claims 1-6.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115240278A (en) * | 2022-09-23 | 2022-10-25 | 东莞先知大数据有限公司 | Fishing behavior detection method |
| CN115497172A (en) * | 2022-11-18 | 2022-12-20 | 合肥中科类脑智能技术有限公司 | Fishing behavior detection method and device, edge processing equipment and storage medium |
| CN115497030A (en) * | 2022-10-27 | 2022-12-20 | 中国水利水电科学研究院 | Fishing behavior identification method based on deep learning |
| CN117292327A (en) * | 2023-11-23 | 2023-12-26 | 安徽启新明智科技有限公司 | Method, device, equipment and medium for associating targets |
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2021
- 2021-12-31 CN CN202111655376.2A patent/CN114445769A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115240278A (en) * | 2022-09-23 | 2022-10-25 | 东莞先知大数据有限公司 | Fishing behavior detection method |
| CN115240278B (en) * | 2022-09-23 | 2023-01-06 | 东莞先知大数据有限公司 | Fishing behavior detection method |
| CN115497030A (en) * | 2022-10-27 | 2022-12-20 | 中国水利水电科学研究院 | Fishing behavior identification method based on deep learning |
| CN115497172A (en) * | 2022-11-18 | 2022-12-20 | 合肥中科类脑智能技术有限公司 | Fishing behavior detection method and device, edge processing equipment and storage medium |
| CN115497172B (en) * | 2022-11-18 | 2023-02-17 | 合肥中科类脑智能技术有限公司 | Fishing behavior detection method and device, edge processing equipment and storage medium |
| CN117292327A (en) * | 2023-11-23 | 2023-12-26 | 安徽启新明智科技有限公司 | Method, device, equipment and medium for associating targets |
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