Aiming at the defects of the foreign object debris(FOD)target detection algorithm of airport runway at the present stage,the improvement of reducing the number of parameters and increasing the accuracy is carried out.Based on you only look once(YOLO)v5s target detection algorithm,a multi-scale feature and attention detection heads lightweight FOD detection algorithm is proposed.Firstly,a new lightweight network structure is proposed.The structure uses depth-separable convolution and point-by-point convolution,and designs a large convolutional kernel architecture to enhance the model sensory field,thus solving the problem of redundancy of many feature maps.Then,the multi-scale feature maps are fused.The number of network parameters is reduced by removing the large target detection layer and adding the small target detection layer,while improving the small target detection capability.Finally,a dynamic head framework is proposed to unify the target detection head and attention,which further improves the network detection accuracy by coherently combining multiple self-attention mechanisms.The experimental results show that the proposed Ghost RepLKNet Dyhead YOLOv5s(GRD-YOLOv5s)network parameter quantity reduced to 3.39 MB,which is only 48%of the original network;the average detection accuracy is improved from 98.40%to 99.45%;the detection speed is 53.42 frames/s.The proposed network provides a new idea to realize the accurate detection of small targets.
维普
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