Object Detection Algorithm Based on Lightweight Convolution and Information Enhancement
In order to solve the problems of large volume,high computational complexity and low pre-cision of target detection algorithm model in mine environment,a target detection algorithm YOLO-AM spe-cially designed for mine environment is proposed.The algorithm adopts the lightweight MobileNetv2 as the backbone network and employs depth-wise separable convolution to replace the 3×3 convolution in the neck network,significantly reducing the model's computational and parameter complexity.This design enables the algorithm to better adapt to the limited computational resources and real-time requirements in mining en-vironments.Furthermore,a coordinate attention mechanism is introduced at the output position of the back-bone network to enhance the effective information in the output features.Simultaneously,a shallow feature enhancement module is proposed,which is integrated into the feature fusion network to augment the seman-tic information of shallow features,thereby improving the model's detection accuracy.Experimental results on the public dataset PASCAL VOC demonstrate that,compared to the baseline model YOLOv4,YOLO-AM achieves a 7%reduction in detection accuracy but significantly reduces parameter complexity by 83%and computational complexity by 86%.Additionally,it enhances detection speed.
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