Road defect detection plays a pivotal role in enhancing traffic safety,optimizing urban management,and fostering sustainable urban development.Nevertheless,the limited availability of detection resources constrains th...Road defect detection plays a pivotal role in enhancing traffic safety,optimizing urban management,and fostering sustainable urban development.Nevertheless,the limited availability of detection resources constrains the deployment and effectiveness of many existing models.To address this challenge,we propose SCD(space-to-depth convolution,ConvTranspose,distance intersection over union(DIoU))-YOLO11s(you only look once version 11 small),an enhanced variant of YOLO11s.The proposed method substantially improves detection accuracy and model adaptability for small-scale defects by integrating the SPD-Conv(space-to-depth convolution)module to capture fine-grained target features,the ConvTranspose module to mitigate resolution degradation of feature maps induced by repeated downsampling,and DIoU loss function to refine multi-scale target localization.Experimental evaluations conducted on the RDD2022 public dataset demonstrate that SCD-YOLO11s achieves a 3.4%improvement in detection accuracy and a 3.5%increase in mAP@0.5%,with only a 1.9 M parameter overhead.These findings highlight the effectiveness and practical significance of the proposed approach in advancing automatic road defect detection systems.展开更多
基金Supported by the National Science Foundation of China(No.62571164)the Natural Science Foundation of Heilongjiang Province(PL2024F025)the Fundamental Scientific Research Funds of Heilongjiang Province(No.2022-KYYWF-1050).
文摘Road defect detection plays a pivotal role in enhancing traffic safety,optimizing urban management,and fostering sustainable urban development.Nevertheless,the limited availability of detection resources constrains the deployment and effectiveness of many existing models.To address this challenge,we propose SCD(space-to-depth convolution,ConvTranspose,distance intersection over union(DIoU))-YOLO11s(you only look once version 11 small),an enhanced variant of YOLO11s.The proposed method substantially improves detection accuracy and model adaptability for small-scale defects by integrating the SPD-Conv(space-to-depth convolution)module to capture fine-grained target features,the ConvTranspose module to mitigate resolution degradation of feature maps induced by repeated downsampling,and DIoU loss function to refine multi-scale target localization.Experimental evaluations conducted on the RDD2022 public dataset demonstrate that SCD-YOLO11s achieves a 3.4%improvement in detection accuracy and a 3.5%increase in mAP@0.5%,with only a 1.9 M parameter overhead.These findings highlight the effectiveness and practical significance of the proposed approach in advancing automatic road defect detection systems.
