Road defect detection algorithm based on improved YOLOv7-tiny
To address the current issues in road damage detection methods,such as large parameter sizes,poor performance in detecting small targets,and high rates of false positives and missed detections,an improved YOLOv7-tiny-based road defect detection algorithm was proposed.The ELAN-SimAM-D structure was designed by introducing depthwise separable convolution(DSC)and a parameter-free attention mechanism,which could reduce computational and parameter sizes to achieve a lightweight model while enhancing the model's feature extraction and fusion capabilities.The SPPAda structure,which incorporated adaptive exponential pooling and adaptive fusion,was introduced as a spatial pyramid pooling structure to enhance the retention of road defect information and improve detection accuracy.A new P2 small target network layer was added to strengthen the detection capability for smaller target defects,improving detection accuracy.A new loss function,NWD-EIOU,was designed to replace the original CIOU loss function,improving the localization accuracy for small targets.Experimental results showed that compared to the original YOLOv7-tiny algorithm,the improved YOLOv7-tiny algorithm achieved an mAP@0.5 of 83.14%on a self-built experimental dataset,an increase of 3.50%,with a 4.96%improvement in recall rate,and a 33.84%reduction in the model's parameter size,meeting the requirements for road defect detection.
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