Research on the application of Yolov5 detection algorithm in coal mine scenarios based on NPU acceleration and video hardware decoding

This paper deeply explores the application of the YOLOv5 detection algorithm based on Neural Processing Unit (NPU) acceleration and video hardware decoding technology in coal mine scenarios. The coal mining industry has long faced the challenges of safe production and intelligent transformation. Whether it is the detection of foreign objects and personnel positioning during underground coal mining, or the coal quality detection in the coal screening and transportation links above ground, accurate and efficient detection technologies are urgently needed. As an advanced algorithm in the field of object detection, when the YOLOv5 algorithm performs detection tasks in the coal mine scenarios, its performance is significantly improved by combining with NPU acceleration technology and video hardware decoding technology. With its powerful parallel computing capabilities, the NPU can rapidly process a large amount of image data, providing strong computing power support for the YOLOv5 algorithm and greatly shortening the processing time of the algorithm. At the same time, the video hardware decoding technology uses hardware acceleration to efficiently convert the video stream into image data available for the algorithm to process, which greatly improves the efficiency of data processing. Experiments show that this method can effectively meet the requirements of rapid and accurate detection of object detection algorithms in coal mine scenarios. It demonstrates extremely high practical value in practical applications and is expected to provide strong technical support for safe production, improving production efficiency, and reducing labor costs in coal mines, thus promoting intelligent development.