An Improved YOLOv5s-based Foreign Object Detection Algorithm for Coal Mine Conveyor Belts
In order to address the issues of large parameter size,high demand for computer resources,and limited types of detected foreign objects in existing detection models for coal mine conveyor belts,YOLOv5s object detection algorithm was optimized.Firstly,a lightweight convolutional neural network,ShuffleNetv2,was adopted as the backbone network of YOLOv5s to extract features from foreign object images,reducing the parameters of the model and improving network parallelism.Secondly,a bidirectional feature pyramid network was used as the feature fusion network to integrate detailed information from different feature map scales.Finally,a coordinate attention mechanism was added to enhance the feature extraction capability,strengthen the focus on foreign object targets,and thereby improve the detection accuracy of network model.Experimental results show that compared to the original model,the improved YOLOv5s-based object detection network model achieved a parameter compression of 3.6×106,and an 8.4%increase in detection frame rate,demonstrating that this method can achieve rapid and accurate foreign object detection on coal mine conveyor belts with fewer computational resources.
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