Optimization algorithm for fine-grained detection of loader materials based on YOLOv5
To address the problem of the lack of high precision detection algorithms for material fine grain aspects in the intelligent shoveling process of loaders,an improved material fine grain object detection algorithm based on YOLOv5 was proposed.This method primarily employed an attention mechanism to enhance the model's capability in extracting fine-grained features and detecting low-quality data.To further leverage attention for optimizing net-work performance,a bilinear attention mechanism was introduced.The optimal embedding scheme was investiga-ted,and the concept of soft thresholding was integrated with the bilinear attention mechanism,aiming to mitigate the impact of low-quality data on the model's detection accuracy.Experimental results demonstrated that compared to the original YOLOv5,the network improved with bilinear attention mechanism achieved a 6.0％increase in mAP@0.5 to 93.2％on high-quality samples,with a Frames Per Second(FPS)of 52.6.After embedding the soft threshold,the network's mAP@0.5 on low-quality samples was improved by 9.9％to 90.2％,with an FPS of 50.0,meeting the requirements for accuracy and real-time performance in the intelligent shovel loading process of load-ers.
万方数据
维普
