Logistics violation behavior identification method and device, equipment and storage medium

Abstract

The invention discloses a logistics violation behavior identification method and device, equipment and a storage medium. The method comprises the steps of: S1, collecting and obtaining a plurality ofpictures of logistics violation behaviors; S2, carrying out data labeling on logistics violation behaviors occurring in the plurality of pictures, and carrying out conversion to generate a picture data set in a TFRecord format; S3, based on a TensorFlow object detection API, training a YOLOv3 model which is converted into a TensorFlow solidification model in advance through a picture data set, verifying the trained YOLOv3 model, and obtaining a violation identification model; and S4, identifying the logistics scene through the violation identification model, and judging whether a logistics violation behavior exists or not to obtain an identification result. According to the invention, the YOLOv3 and the TensorFlow object detection API are combined and applied to the field of logistics to carry out violation behaviors; the invention greatly improves the training speed of the model and the precision and speed in the training and supervision process, guarantees the interests of the user,provides the service quality of the user, reduces the loss of the logistics industry, improves the transfer efficiency and quality, and enables the logistics to be closer on an intelligent road.

Description

technical field [0001] The invention belongs to the technical field of logistics intelligence, and in particular relates to a method, device, equipment and storage medium for identifying logistics violations. Background technique [0002] Object detection is a popular direction in computer vision and digital image processing. It is widely used in robot navigation, intelligent video surveillance, industrial inspection, aerospace and many other fields. It has important practical significance to reduce the consumption of human capital through computer vision. Therefore, target detection has become a research hotspot in theory and application in recent years. It is an important branch of image processing and computer vision, and it is also the core part of intelligent monitoring systems. At the same time, target detection is also a basic in the field of pan-identification Algorithms play a vital role in subsequent tasks such as face recognition, gait recognition, crowd counting,...

AI Technical Summary

Problems solved by technology

However, in actual operation, there will always be some unsatisfactory situations, such as the occurrence of logistics violations such as trampling on by operators, package damage caused by throwing, etc.

Although target detection can play a certain regulatory role in the actual process, due to the uncontrollability of logistics personnel, once new logistics violations are exposed, it is necessary to go through the process of collecting resources, building models, and model training again. This process has problems such as long preparation time, slow model training, and poor supervision timeliness, which is not conducive to the improvement of logistics supervision and logistics quality

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