刘浩文
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论文成果
一种端到端实时道路损伤检测算法
- 发布时间：2025-10-23
- 点击次数：
- 发表刊物：PRCV2025
- 摘要：Road surfaces are prone to cracks and potholes due to environmental variations and material aging. Existing object detection algorithms exhibit inadequate adaptability to morphological diversity, feature ambiguity, and environmental interference in road damage, coupled with a persistent reliance on post-processing steps. To address these challenges, we propose an end-to-end real-time road damage detection
algorithm named RDD-DETR. To address feature ambiguity and background interference, an image enhancement method named M-USM and a residual context enhancement module (RCE Block) are designed to strengthen feature extraction capabilities in complex scenarios. A deformable attention-based feature interaction (DAIFI) module is constructed to optimize high-level feature interaction, while a cross-guided attention fusion (CGAF) module is established to achieve adaptive multiscale fusion through bidirectional feature complementarity. The end-toend
architecture is implemented to eliminate the dependency on NMS
post-processing inherent in existing YOLO-series algorithms. Experiments demonstrate that RDD-DETR achieves a 5.2% improvement in accuracy over baseline while maintaining speed comparable to YOLOseries algorithms. - 备注：the 8th Chinese Conference on Pattern Recognition and Computer Vision (PRCV 2025), Shanghai, October 15-18, 2025
- 合写作者：赵卓轩,樊胜华
- 论文类型：论文集
- 通讯作者：陈曦,刘浩文
- 学科门类：工学
- 文献类型：C
- 是否译文：否
- 发表时间：2025-10-15
- 收录刊物：SCI