Design of Coal Mine Conveyor Belt Foreign Body Detection System Based on Machine Vision

This research is carried out from four aspects: data preparation, algorithm design, system development and performance evaluation. In the data preparation stage, a large number of pictures of foreign objects on the coal mine conveyor belt were collected, and Labelme was used to manually label them. The algorithm design adopts the deep learning method based on YOLOv8n, and replaces the traditional convolution kernel with GhostConv to create a YOLOv8G model, which can accurately identify and locate foreign objects on the conveyor belt. The test results show that the model has high detection accuracy and real-time performance, which can meet the safety needs of coal mine transportation, provide all-weather automatic monitoring solutions, help to deal with foreign objects in time, and promote the stability and reliability of coal mine transportation environment.<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup>