CN117197787A - Intelligent security inspection method, device, equipment and medium based on improved YOLOv5 - Google Patents
Intelligent security inspection method, device, equipment and medium based on improved YOLOv5 Download PDFInfo
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Abstract
The application discloses an intelligent security inspection method, device, equipment and medium based on improved YOLOv 5. The method comprises the following steps of constructing an improved YOLOv5 model based on a Biformer attention module and a decoupling head, inputting a data set into the improved YOLOv5 model for training until the iteration times are reached or preset requirements are met, outputting an identification model and storing the identification model; and acquiring an X-ray security inspection image to be identified, inputting the X-ray security inspection image to be identified into an identification model, and outputting an identification result, wherein the identification result comprises position information and category information of contraband in the X-ray security inspection image. The application improves the detection speed and the detection accuracy of security inspection and reduces the cost of security inspection personnel for enterprises and units.
Description
Technical Field
The application relates to the technical field of image processing, in particular to an intelligent security inspection method, device, equipment and medium based on improved YOLOv 5.
Background
With the development of various public transportation technologies, the travel safety of passengers is also receiving extensive attention from the society. The security inspection is a firewall which is required to be arranged in order to protect the safety of passengers in the industry nowadays, but under the condition of more people flow, the traditional security inspection depends on manual work, the working strength of staff is high, unavoidable bottlenecks can occur in the inspection speed, the congestion of the staff in stations and airports is caused, the travel efficiency of the passengers is greatly influenced, and the hidden danger of omission inspection and false inspection also exists. If the target detection technology and the security inspection technology can be combined, the efficiency and the accuracy of detection can be improved, the labor cost is saved for airports and stations, and better travel experience is provided for passengers.
The existing security inspection technology mainly comprises the steps that security personnel carry out visual inspection, passengers place baggage on a conveyor belt of a security inspection machine, the baggage is waited to be sent into an X-ray detection area, the security inspection machine uses X-ray imaging for the baggage of the passengers to obtain an X-ray image of the baggage, and the security inspection personnel carry out visual identification and detection for the X-ray image of the baggage to judge whether contraband exists in the X-ray image of the baggage. The detection is completely dependent on manual work, the personnel culture cost is high, under the high-strength working condition, the detection efficiency has the bottleneck, the possibility of missed detection and false detection, and the high-efficiency and accurate detection under a small sample is difficult to realize by using the current model when the automatic security inspection is carried out based on the machine vision method.
Disclosure of Invention
In order to solve the technical problems, the intelligent security inspection method, device, equipment and medium based on the improved YOLOv5 are provided, so that the detection speed and the detection accuracy of security inspection can be improved, and the cost of security inspection personnel for enterprises and institutions is reduced.
In order to achieve the above purpose, the technical scheme of the application is as follows:
an intelligent security inspection method based on improved YOLOv5 comprises the following steps:
constructing an improved YOLOv5 model based on a Biformer attention module and a decoupling head, inputting a data set into the improved YOLOv5 model for training until the iteration number is reached or the preset requirement is met, outputting an identification model and storing the identification model;
and acquiring an X-ray security inspection image to be identified, inputting the X-ray security inspection image to be identified into an identification model, and outputting an identification result, wherein the identification result comprises position information and category information of contraband in the X-ray security inspection image.
Preferably, the training process further comprises the steps of:
and carrying out data enhancement processing on the data set by adopting a Mixup algorithm.
Preferably, the data set obtaining method includes the following steps:
collecting a plurality of X-ray security inspection images, and labeling the plurality of X-ray security inspection images;
and constructing a data set based on the marked X-ray security inspection image, wherein the data set comprises a training set and a verification set.
Preferably, the improved YOLOv5 model is based on a YOLOv5s model, and a Biformer attention module is introduced into a backbone network and is used for performing sparse sampling type feature extraction on contraband in an input image; and introducing a decoupling head into the detection head for respectively carrying out different convolution operations on the input characteristic diagrams.
Based on the above, the application also discloses an intelligent security inspection device based on improved YOLOv5 and based on improved YOLOv5, which comprises a training module and a security inspection identification module, wherein,
the training module is used for constructing an improved YOLOv5 model based on the Biformer attention module and the decoupling head, inputting a preset data set into the improved YOLOv5 model for training until the iteration times are reached or the preset requirements are met, outputting an identification model and storing the identification model;
the security inspection identification module is used for acquiring an X-ray security inspection image to be identified, inputting the X-ray security inspection image to be identified into the identification model, and outputting an identification result, wherein the identification result comprises position information and category information of contraband.
Preferably, the training module is further configured to perform data enhancement processing on the data set by adopting a mix up algorithm.
Preferably, the data set obtaining method includes the following steps:
collecting a plurality of X-ray security inspection images, and labeling the plurality of X-ray security inspection images;
and constructing a data set based on the marked X-ray security inspection image, wherein the data set comprises a training set and a verification set.
Preferably, the improved YOLOv5 model is based on a YOLOv5s model, and a Biformer attention module is introduced into a backbone network and is used for performing sparse sampling type feature extraction on contraband in an input image; and introducing a decoupling head into the detection head for respectively carrying out different convolution operations on the input characteristic diagrams.
Based on the above, the present application also discloses a computer device, including: a memory for storing a computer program; a processor for implementing a method as claimed in any one of the preceding claims when executing the computer program.
Based on the foregoing, the present application also discloses a readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as described in any of the above.
Based on the technical scheme, the application has the beneficial effects that: an improved YOLOv5 model based on a BiFormer attention module and a decoupling head is constructed based on an intelligent security inspection method of the improved YOLOv5, a data set is input into the improved YOLOv5 model for training until the iteration times are reached or preset requirements are met, and an identification model is output and stored; the method comprises the steps of obtaining an X-ray security inspection image to be identified, inputting the X-ray security inspection image to be identified into an identification model, and outputting an identification result, wherein the identification result comprises position information and category information of contraband articles in the X-ray security inspection image, and can accurately identify the contraband articles such as tools, lighters, charging treasures and the like in the X-ray image. The application can clearly obtain the position information and the category information of the contraband, and is convenient for security check personnel to carry out unpacking check work on the luggage.
Drawings
FIG. 1 is a schematic illustration of an application environment of an intelligent security method based on improved YOLOv5 in one embodiment;
FIG. 2 is a flow diagram of an intelligent security method based on improved YOLOv5 in one embodiment;
FIG. 3 is a schematic diagram of the structure of an improved YOLOv5 model in one embodiment;
FIG. 4 is a schematic diagram of the structure of an intelligent security apparatus based on improved YOLOv5 in one embodiment;
FIG. 5 is an internal block diagram of a computer device in one embodiment.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application.
The intelligent security inspection method based on the improved YOLOv5 provided by the embodiment of the application can be applied to an application environment shown in figure 1. As shown in FIG. 1, the application environment includes a computer device 110. The computer device 110 may construct an improved YOLOv5 model based on the BiFormer attention module and the decoupling head, input the dataset into the improved YOLOv5 model for training until the number of iterations is reached or the preset requirement is met, output the recognition model and save; the computer device 110 may acquire an X-ray security image to be identified and input the X-ray security image to be identified into an identification model, and output an identification result, where the identification result includes location information and category information of contraband in the X-ray security image. The computer device 110 may be, but is not limited to, a security check machine, various personal computers, notebook computers, smart phones, robots, unmanned aerial vehicles, tablet computers, and the like.
In one embodiment, as shown in fig. 2, an improved YOLOv 5-based intelligent security method is provided, comprising the steps of:
step 210, constructing an improved YOLOv5 model based on a Biformer attention module and a decoupling head, inputting a data set into the improved YOLOv5 model for training until the iteration times are reached or preset requirements are met, outputting an identification model and storing the identification model.
The computer device can build an improved YOLOv5 model based on the bifomer attention mechanism module and the Dcoupled decoupling head. The improved YOLOv5 model is based on the YOLOv5s model, a BiFormer attention mechanism is introduced, sparse sampling type feature extraction is carried out on contraband, a target area with the contraband as the center is strengthened and highlighted, a directed graph of the relevant area is constructed, and the recognition capability of the model to small targets is improved; the tasks of classification and positioning in the model identification are separated by the decomplexed decoupling heads, different convolution operations are respectively carried out on the feature graphs input by the upper network to highlight the features required by the respective tasks, so that different tasks are served, and the model reasoning confidence and positioning accuracy are improved, as shown in fig. 3.
The security inspection X-ray contraband images are of 12 types in total and are respectively a pistol, a fruit knife, a blade, a screwdriver, scissors, a liquid material, pliers, a lighter, a spanner, a mobile power supply, a hammer and a fork. The computer device can acquire X-ray contraband images or X-ray imaging generated by security inspection machine histories aiming at the detected object from the website, and the images are marked to construct a data set. Training the improved YOLOv5 model based on the data set until the iteration times are reached or the preset requirements are met, and obtaining an identification model for security inspection contraband identification.
Step 220, acquiring an X-ray security inspection image to be identified, inputting the X-ray security inspection image to be identified into an identification model, and outputting an identification result, wherein the identification result comprises position information and category information of contraband in the X-ray security inspection image.
In the embodiment, the identification model is embedded in security inspection equipment such as a security inspection machine, after the security inspection machine performs X-ray imaging on a detected object, one image is intercepted and input into the identification model, synchronous detection and identification are performed on the obtained image, the position of contraband and class information thereof are highlighted by a square frame, and the result is output on a monitor for workers to perform next inspection work.
In one embodiment, a training process based on the intelligent security inspection method of improved YOLOv5 is also provided, and specifically includes the following steps: and carrying out data enhancement processing on the data set by adopting a Mixup algorithm.
According to the embodiment, a Mixup data enhancement technology is adopted, two pictures can be mixed in proportion, the penetration characteristic of an X-ray picture is simulated, the distribution situation of contraband of model training is expanded, and the recognition capability of images is enhanced.
The competition data set of the challenge competition is identified by using the X-ray security check image held by the scientific mass-information flyer in 2020 for training and testing, and the comparison with the original model is shown in a table-1.
Table-1 comparison of effects before and after improvement
| Scheme for the production of a semiconductor device | Precision | Recall | mAP@.5 | mAP@.5:.95 |
| Yolov5 | 0.811 | 0.629 | 0.705 | 0.489 |
| Improved YOLOv5 | 0.832 | 0.679 | 0.742 | 0.509 |
Precision (Precision), recall (Recall), and average Precision (mAP) were used as evaluation indexes for improving the YOLOv5 model. According to experimental data analysis, the improved Yolov5 and the original model Yolov5 are obviously improved in Precision, recall, mAP@5 and mAP@5:95 indexes.
In one embodiment, a process for providing a data set acquisition mode in the intelligent security inspection method based on improved YOLOv5 is further provided, which specifically includes the following steps: collecting a plurality of X-ray security inspection images, and labeling the plurality of X-ray security inspection images; and constructing a data set based on the marked X-ray security inspection image, wherein the data set comprises a training set and a verification set.
As shown in fig. 4, in one embodiment, an improved YOLOv 5-based intelligent security apparatus 300 is provided that is based on improved YOLOv5, including a training module 310 and a security identification module 320, wherein,
the training module 310 is configured to construct an improved YOLOv5 model based on the BiFormer attention module and the decoupling head, input a preset data set into the improved YOLOv5 model for training until the iteration number is reached or the preset requirement is met, output an identification model and store the identification model;
the security inspection identification module 320 is configured to obtain an X-ray security inspection image to be identified, input the X-ray security inspection image to be identified into an identification model, and output an identification result, where the identification result includes location information and category information of contraband.
In one embodiment, the training module 310 is further configured to perform data enhancement processing on the data set using a mix up algorithm. By adopting the Mixup data enhancement technology, two pictures can be mixed in proportion, the penetration characteristic of an X-ray picture is simulated, the distribution situation of contraband of model training is expanded, and the recognition capability of images is enhanced.
In one embodiment, the training module 310 is further configured to collect a plurality of X-ray security images and label the plurality of X-ray security images; and constructing a data set based on the marked X-ray security inspection image, wherein the data set comprises a training set and a verification set.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements an improved YOLOv 5-based intelligent security method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in FIG. 5 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
constructing an improved YOLOv5 model based on a Biformer attention module and a decoupling head, inputting a data set into the improved YOLOv5 model for training until the iteration number is reached or the preset requirement is met, outputting an identification model and storing the identification model;
and acquiring an X-ray security inspection image to be identified, inputting the X-ray security inspection image to be identified into an identification model, and outputting an identification result, wherein the identification result comprises position information and category information of contraband in the X-ray security inspection image.
In one embodiment, the processor when executing the computer program further performs the steps of: and carrying out data enhancement processing on the data set by adopting a Mixup algorithm.
In one embodiment, the processor when executing the computer program further performs the steps of: collecting a plurality of X-ray security inspection images, and labeling the plurality of X-ray security inspection images; and constructing a data set based on the marked X-ray security inspection image, wherein the data set comprises a training set and a verification set.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
constructing an improved YOLOv5 model based on a Biformer attention module and a decoupling head, inputting a data set into the improved YOLOv5 model for training until the iteration number is reached or the preset requirement is met, outputting an identification model and storing the identification model;
and acquiring an X-ray security inspection image to be identified, inputting the X-ray security inspection image to be identified into an identification model, and outputting an identification result, wherein the identification result comprises position information and category information of contraband in the X-ray security inspection image.
In one embodiment, the computer program when executed by the processor further performs the steps of: and carrying out data enhancement processing on the data set by adopting a Mixup algorithm.
In one embodiment, the computer program when executed by the processor further performs the steps of: collecting a plurality of X-ray security inspection images, and labeling the plurality of X-ray security inspection images; and constructing a data set based on the marked X-ray security inspection image, wherein the data set comprises a training set and a verification set.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. The intelligent security inspection method based on the improved YOLOv5 is characterized by comprising the following steps of:
constructing an improved YOLOv5 model based on a Biformer attention module and a decoupling head, inputting a data set into the improved YOLOv5 model for training until the iteration number is reached or the preset requirement is met, outputting an identification model and storing the identification model;
and acquiring an X-ray security inspection image to be identified, inputting the X-ray security inspection image to be identified into an identification model, and outputting an identification result, wherein the identification result comprises position information and category information of contraband in the X-ray security inspection image.
2. The improved YOLOv 5-based intelligent security method of claim 1, wherein the training process further comprises the steps of:
and carrying out data enhancement processing on the data set by adopting a Mixup algorithm.
3. The intelligent security inspection method based on improved YOLOv5 of claim 2, wherein the data set acquisition mode comprises the following steps:
collecting a plurality of X-ray security inspection images, and labeling the plurality of X-ray security inspection images;
and constructing a data set based on the marked X-ray security inspection image, wherein the data set comprises a training set and a verification set.
4. The intelligent security inspection method based on improved YOLOv5 of claim 3, wherein the improved YOLOv5 model is based on YOLOv5s model, and a bifomer attention module is introduced into a backbone network for sparse sampling feature extraction of contraband in an input image; and introducing a decoupling head into the detection head for respectively carrying out different convolution operations on the input characteristic diagrams.
5. The intelligent security inspection device based on the improved YOLOv5 is characterized by comprising a training module and a security inspection identification module, wherein,
the training module is used for constructing an improved YOLOv5 model based on the Biformer attention module and the decoupling head, inputting a preset data set into the improved YOLOv5 model for training until the iteration times are reached or the preset requirements are met, outputting an identification model and storing the identification model;
the security inspection identification module is used for acquiring an X-ray security inspection image to be identified, inputting the X-ray security inspection image to be identified into the identification model, and outputting an identification result, wherein the identification result comprises position information and category information of contraband.
6. The improved YOLOv 5-based intelligent security apparatus of claim 5, wherein the training module is further configured to perform data enhancement processing on the data set using a mix up algorithm.
7. The intelligent security apparatus based on improved YOLOv5 of claim 6, wherein the data set acquisition mode comprises the following steps:
collecting a plurality of X-ray security inspection images, and labeling the plurality of X-ray security inspection images;
and constructing a data set based on the marked X-ray security inspection image, wherein the data set comprises a training set and a verification set.
8. The intelligent security inspection device based on improved YOLOv5 of claim 6, wherein the improved YOLOv5 model is based on YOLOv5s model, and a bifomer attention module is introduced into a backbone network for sparse sampling feature extraction of contraband in an input image; and introducing a decoupling head into the detection head for respectively carrying out different convolution operations on the input characteristic diagrams.
9. A computer device, comprising: a memory for storing a computer program; a processor for implementing the method according to any one of claims 1 to 4 when executing the computer program.
10. A readable storage medium, characterized in that it has stored thereon a computer program which, when executed by a processor, implements the method according to any of claims 1 to 4.
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