CN112001228A - Video monitoring warehouse in-out counting system and method based on deep learning - Google Patents
Video monitoring warehouse in-out counting system and method based on deep learning Download PDFInfo
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Abstract
The invention discloses a video monitoring warehouse in-out counting system and method based on deep learning, and relates to the technical field of deep learning. The system comprises a monitoring video acquisition module, a video processing module, an article detection and identification module, an in-out warehouse logic judgment module and a visual management recording module; the method comprises the following steps: s1, collecting monitoring videos; s2, collecting and dividing the video; s3, manual marking; s4, respectively carrying out detection model training and classification model training; s5, determining the position and the number of the vehicle-mounted articles; s6, accurately identifying the articles; and S7, identifying, recording and displaying the vehicle information. The invention can record the in-out warehouse conveying pictures in real time, avoid the situation that the pictures can be found without trace in the subsequent warehouse checking process, does not depend on the participation of people in the whole process, and ensures the high efficiency, the real-time property, the accuracy and the intellectualization of the warehouse in-out warehouse article supervision by accurately detecting and identifying the articles in the video pictures and recording the types and the quantity of the in-out warehouse articles in real time.
Description
Technical Field
The invention belongs to the technical field of deep learning, and particularly relates to a video monitoring warehouse in-out counting system based on deep learning and a video monitoring warehouse in-out counting method based on deep learning.
Background
For the solid product industry, warehouse management is an important component of enterprise management, and because of a large number of product types, a large amount of data needs to be processed quickly and accurately every day when the products are frequently put in and taken out of a warehouse, otherwise, the business development of the enterprise is influenced, the warehouse management is always an important and arduous task for the enterprise.
With the introduction and popularization of network and computer technologies and deep learning, the rapid development and the falling of artificial intelligence are driven, so that management of enterprises in aspects of warehouse inventory checking, warehouse entry and exit supervision and the like is more convenient and intelligent, the business processing efficiency, accuracy and real-time performance of warehouse workers are greatly improved, and the method has important significance for modern enterprise warehouse management.
The traditional warehouse-in and warehouse-out supervision method usually depends on human to carry out accounting, records the types of products, the quantity of the products and the warehouse-in and warehouse-out time, then carries out final warehouse inventory checking and proofreading in a warehouse management system, and aims at the warehouses such as raw materials and processing factories, the raw material conveying frequency is high, the warehouse-in and warehouse-out time is random, the requirement on human is strict, the checking and accounting can not be carried out in real time when the warehouse-in and warehouse-out time is carried out when the warehouse-in and warehouse-out time is called, and a large amount of manpower.
In recent years, a solution is proposed for managing an entrance and exit warehouse, an infrared code scanning gun is used for recording articles, the article types can be distinguished, and the quantity can be recorded, but the method needs to paste two-dimensional codes on the articles in advance and needs to be operated by people in the field, the method increases the cost, and the participation of people cannot be completely avoided; therefore, the video monitoring warehouse in-out counting system and method based on deep learning are significant for solving the problems.
Disclosure of Invention
The invention provides a video monitoring warehouse in-out counting system and method based on deep learning, which solve the problems.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention relates to a video monitoring warehouse in-out counting system based on deep learning, which comprises:
the monitoring video acquisition module: the system is used for acquiring video pictures of vehicles entering and leaving the warehouse at the door of the warehouse in real time;
the video processing module: the monitoring video acquisition module is used for acquiring a video picture sent by the monitoring video acquisition module and sending the received video picture to the article detection and identification module;
article detect identification module: the system is used for detecting and identifying the type and the quantity of the articles in the video picture sent by the video processing module;
and an out-in-warehouse logic judgment module: the system is used for judging the state of warehouse-out or warehouse-in and recording the specific time according to the article detection and identification result of the continuous frame video picture;
the visual management recording module: the front-end visual display system is used for recording photos of the articles in the warehouse and the types and the quantity of the articles and carrying out front-end visual display.
A video monitoring warehouse in-out counting method based on deep learning comprises the following steps:
s1, collecting monitoring videos: the monitoring video acquisition module acquires videos of vehicles loaded in and out of the warehouse at the door of the warehouse in real time, and adopts a video monitoring camera mechanism;
s2, collecting video and dividing: the video processing module divides the collected video into image data according to the number of frames;
s3, manual marking: the image data divided according to the number of frames is marked manually, and the position of a vehicle and the position of an article in a video picture are marked;
s4, respectively carrying out detection model training and classification model training: training a target detection model and a classification model of deep learning by using a deep learning target detection algorithm respectively for the image data after the manual marking to form an article detection and identification module;
s5, determining the position and the number of the vehicle-mounted articles: determining positions and quantities of warehouse entrance and exit vehicles and articles by using the trained target detection model;
s6, accurate article identification: identifying a specific category of the item using a classification model;
s7, recognizing, recording and displaying vehicle information: the vehicle traveling direction is judged by identifying the continuous pictures of the vehicle-mounted articles passing through the door of the warehouse, and the vehicle traveling direction is recorded as the warehouse-out action or the warehouse-in action.
Further, the training of the target detection model in step S4 includes issuing a neural network model according to a target detection general algorithm, and training the detection model to detect the positions of the vehicle and the object in the image according to the actual labeled information.
Further, the training of the classification model in step S4 includes building a neural network model according to a classification model common algorithm, and training the classification model to identify the type of the item according to the actual item type.
Further, the precise item identification in step S6 is specifically to identify the position of the item detected by the detection model, and use the classification model to identify the position in the frame, so as to distinguish the type of the goods.
Further, the identifying, recording and displaying the vehicle information in the step S7 specifically includes: recording the vehicle positions of continuous frames from the time when the vehicle enters the video picture to the time when the vehicle enters the garage and exits the garage; when the vehicle-mounted article appears in the monitoring picture, starting to record the position of the vehicle in the picture, continuously recording until the vehicle-mounted article finishes the operation of entering and exiting the garage, judging the vehicle advancing direction in the time period, and judging that the vehicle-mounted article is the operation of exiting the garage or entering the garage in the time period; recording the in-out frame and the article type number detected each time, and automatically sending the in-out frame and the article type number to a front-end in-out management page; and carrying out visual display.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a video monitoring warehouse in-out counting system and method based on deep learning aiming at the defects of the traditional warehouse in-out and warehouse-in monitoring method, the method adopts image data based on warehouse in-out sites to detect and identify goods, judges whether the goods are put in or out of the warehouse according to the direction and the track of the vehicle, and can record the delivery pictures of the warehouse in and out in real time, avoid the unscented searching in the subsequent warehousing and checking process, the whole monitoring counting process of the warehouse-in and warehouse-out article monitoring management does not depend on the participation of people at all, the articles in the video pictures shot by the monitoring equipment are identified through accurate detection, the types and the number of the articles in the warehouse are recorded in real time, the high efficiency, the real-time performance, the accuracy, the intellectualization and the visualization of the warehouse entry and exit article supervision are ensured, the manpower and the material resources are saved, and objective, high-efficiency and accurate warehouse entry and exit counting management can be achieved.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, 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 invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of a video surveillance warehouse in-out counting system based on deep learning according to the present invention;
fig. 2 is a flowchart of the method for counting the warehouse-in and warehouse-out of the video surveillance warehouse based on deep learning.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, 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 invention.
Referring to fig. 1, a video monitoring warehouse entry and exit counting system based on deep learning according to the present invention includes:
the monitoring video acquisition module: the system is used for acquiring video pictures of vehicles entering and leaving the warehouse at the door of the warehouse in real time;
the video processing module: the system comprises a monitoring video acquisition module, an article detection and identification module, a video acquisition module, a video display module and a video display module, wherein the monitoring video acquisition module is used for acquiring a video image sent by the monitoring video acquisition module and sending the received video image to the article detection and identification module;
article detect identification module: the device is used for detecting and identifying the type and the quantity of the articles in the video picture sent by the video processing module;
and an out-in-warehouse logic judgment module: the system is used for judging the state of warehouse-out or warehouse-in and recording the specific time according to the article detection and identification result of the continuous frame video picture;
the visual management recording module: the front-end visual display system is used for recording photos of the articles in the warehouse and the types and the quantity of the articles and carrying out front-end visual display.
As shown in fig. 2, a method for counting warehouse in and out of a video surveillance warehouse based on deep learning is characterized by comprising the following steps:
s1, collecting monitoring videos: the monitoring video acquisition module acquires videos of vehicles loaded in and out of the warehouse at the door of the warehouse in real time, and adopts a video monitoring camera mechanism;
s2, collecting video and dividing: the video processing module divides the collected video into image data according to the number of frames;
s3, manual marking: the image data divided according to the number of frames is marked manually, and the position of a vehicle and the position of an article in a video picture are marked;
s4, respectively carrying out detection model training and classification model training: training a target detection model and a classification model of deep learning by using a deep learning target detection algorithm respectively for the image data after the manual marking to form an article detection and identification module;
s5, determining the position and the number of the vehicle-mounted articles: determining positions and quantities of warehouse entrance and exit vehicles and articles by using the trained target detection model;
s6, accurate article identification: identifying a specific category of the item using a classification model;
s7, recognizing, recording and displaying vehicle information: the vehicle traveling direction is judged by identifying the continuous pictures of the vehicle-mounted articles passing through the door of the warehouse, and the vehicle traveling direction is recorded as the warehouse-out action or the warehouse-in action.
In step S4, the training of the target detection model includes issuing a neural network model according to a target detection common algorithm, and training the detection model to detect the positions of the vehicle and the object in the image according to the actual labeled information.
The training of the classification model in step S4 includes building a neural network model according to a classification model common algorithm, and training the classification model to identify the type of the article according to the actual article type.
In step S6, the precise item identification is specifically to identify the position of the item detected by the detection model, and identify the position in the picture by using the classification model to distinguish the type of the goods.
The step S7 of identifying, recording, and displaying the vehicle information specifically includes: recording the vehicle positions of continuous frames from the time when the vehicle enters the video picture to the time when the vehicle enters the garage and exits the garage; when the vehicle-mounted article appears in the monitoring picture, starting to record the position of the vehicle in the picture, continuously recording until the vehicle-mounted article finishes the operation of entering and exiting the garage, judging the vehicle advancing direction in the time period, and judging that the vehicle-mounted article is the operation of exiting the garage or entering the garage in the time period; recording the in-out frame and the article type number detected each time, and automatically sending the in-out frame and the article type number to a front-end in-out management page; and carrying out visual display.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a video monitoring warehouse in-out counting system and method based on deep learning aiming at the defects of the traditional warehouse in-out and warehouse-in monitoring method, the method adopts image data based on warehouse in-out sites to detect and identify goods, judges whether the goods are put in or out of the warehouse according to the direction and the track of the vehicle, and can record the delivery pictures of the warehouse in and out in real time, avoid the unscented searching in the subsequent warehousing and checking process, the whole monitoring counting process of the warehouse-in and warehouse-out article monitoring management does not depend on the participation of people at all, the articles in the video pictures shot by the monitoring equipment are identified through accurate detection, the types and the number of the articles in the warehouse are recorded in real time, the high efficiency, the real-time performance, the accuracy, the intellectualization and the visualization of the warehouse entry and exit article supervision are ensured, the manpower and the material resources are saved, and objective, high-efficiency and accurate warehouse entry and exit counting management can be achieved.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (6)
1. A video surveillance warehouse in-out counting system based on deep learning is characterized by comprising:
the monitoring video acquisition module: the system is used for acquiring video pictures of vehicles entering and leaving the warehouse at the door of the warehouse in real time;
the video processing module: the monitoring video acquisition module is used for acquiring a video picture sent by the monitoring video acquisition module and sending the received video picture to the article detection and identification module;
article detect identification module: the system is used for detecting and identifying the type and the quantity of the articles in the video picture sent by the video processing module;
and an out-in-warehouse logic judgment module: the system is used for judging the state of warehouse-out or warehouse-in and recording the specific time according to the article detection and identification result of the continuous frame video picture;
the visual management recording module: the front-end visual display system is used for recording photos of the articles in the warehouse and the types and the quantity of the articles and carrying out front-end visual display.
2. The method of claim 1 for applying the in-warehouse counting system to a deep learning-based video surveillance warehouse in-warehouse counting method, comprising the steps of:
s1, collecting monitoring videos: the monitoring video acquisition module acquires videos of vehicles loaded in and out of the warehouse at the door of the warehouse in real time, and adopts a video monitoring camera mechanism;
s2, collecting video and dividing: the video processing module divides the collected video into image data according to the number of frames;
s3, manual marking: the image data divided according to the number of frames is marked manually, and the position of a vehicle and the position of an article in a video picture are marked;
s4, respectively carrying out detection model training and classification model training: training a target detection model and a classification model of deep learning by using a deep learning target detection algorithm respectively for the image data after the manual marking to form an article detection and identification module;
s5, determining the position and the number of the vehicle-mounted articles: determining positions and quantities of warehouse entrance and exit vehicles and articles by using the trained target detection model;
s6, accurate article identification: identifying a specific category of the item using a classification model;
s7, recognizing, recording and displaying vehicle information: the vehicle traveling direction is judged by identifying the continuous pictures of the vehicle-mounted articles passing through the door of the warehouse, and the vehicle traveling direction is recorded as the warehouse-out action or the warehouse-in action.
3. The method of claim 2, wherein the training of the target detection model in step S4 includes issuing a neural network model according to a target detection algorithm, and training the detection model to detect the positions of the vehicles and the objects in the images according to the actual labeled information.
4. The method as claimed in claim 2, wherein the training of the classification model in step S4 includes building a neural network model according to a classification model common algorithm, and training the classification model to identify the type of the item according to the actual item type.
5. The method as claimed in claim 2, wherein the precise item identification in step S6 is specifically an item position detected by a detection model, and the position in the frame is identified by using a classification model to distinguish the type of goods.
6. The method as claimed in claim 2, wherein the step S7 of identifying, recording and displaying vehicle information includes: recording the vehicle positions of continuous frames from the time when the vehicle enters the video picture to the time when the vehicle enters the garage and exits the garage; when the vehicle-mounted article appears in the monitoring picture, starting to record the position of the vehicle in the picture, continuously recording until the vehicle-mounted article finishes the operation of entering and exiting the garage, judging the vehicle advancing direction in the time period, and judging that the vehicle-mounted article is the operation of exiting the garage or entering the garage in the time period; recording the in-out frame and the article type number detected each time, and automatically sending the in-out frame and the article type number to a front-end in-out management page; and carrying out visual display.
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