Coal mine personnel behavior detection method, equipment and storage medium

Abstract

The embodiment of the present application provides a coal mine personnel behavior detection method, equipment and storage medium. The method includes acquiring an image to be processed, and performing preprocessing on the image to be processed, and obtaining an input tag sequence corresponding to the image to be processed. According to the input tag sequence, based on The attention algorithm determines the attention output matrix, and generates a feature map according to the attention output matrix, performs bounding box regression processing on the feature map, and obtains the behavior classification results of coal mine personnel in each bounding box. In the embodiment of the present application, by performing global image processing on the image to be processed, compared with only extracting human body node information, it includes more detailed information features, so that the output coal mine personnel's behavior classification results are more accurate, and by using bounding boxes The regression algorithm can detect the behaviors of multiple people in the image at the same time, and obtain the behavior classification results corresponding to the people in each bounding box, ensuring the comprehensiveness of the detection results.

Description

technical field [0001] The embodiments of the present application relate to the technical field of image processing, and in particular to a coal mine personnel behavior detection method, device and storage medium. Background technique [0002] With the improvement of video data processing capability and the development of computer vision technology, the performance requirements of coal mine intelligent video surveillance system are getting higher and higher. Through the coal mine intelligent video monitoring system, real-time monitoring of the coal mine production site can be carried out, and unsafe working factors can be found as early as possible to ensure the safety of the coal mine production environment. [0003] In the existing technology, the coal mine intelligent video surveillance system can use the key point detection method based on deep learning to extract the human body node information of the coal mine personnel, and realize the detection of the coal mine perso...

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Problems solved by technology

[0004] However, in the process of implementing this application, the inventors found that there are at least the following problems in the prior art: in the existing behavior detection scheme, the behavior detection of coal mine personnel is only based on the extracted human node information, and the data base is relatively weak. In the case of complex and diverse behavior types, the accuracy of behavior detection results is low

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