Zuo Cuihua, Huang Nengxin, Yuan Cao, Li Yaqin
School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430024, China.
Sensors (Basel). 2025 Apr 11;25(8):2426. doi: 10.3390/s25082426.
The accurate detection of road defects is crucial for enhancing the safety and efficiency of road maintenance. This study focuses on six common types of pavement defects: transverse cracks, longitudinal cracks, alligator cracking, oblique cracks, potholes, and repair marks. In real-world scenarios, key challenges include effectively distinguishing between the foreground and background, as well as accurately identifying small-sized (e.g., fine cracks, dense alligator cracking, and clustered potholes) and overlapping defects (e.g., intersecting cracks or clustered damage areas where multiple defects appear close together). To address these issues, this paper proposes a Pavement-DETR model based on the Real-Time Detection Transformer (RT-DETR), aiming to optimize the overall accuracy of defect detection. To achieve this goal, three main improvements are proposed: (1) the introduction of the Channel-Spatial Shuffle (CSS) attention mechanism in the third (S3) and fourth (S4) stages of the ResNet backbone, which correspond to mid-level and high-level feature layers, enabling the model to focus more precisely on road defect features; (2) the adoption of the Conv3XC structure for feature fusion enhances the model's ability to differentiate between the foreground and background, which is achieved through multi-level convolutions, channel expansion, and skip connections, which also contribute to improved gradient flow and training stability; (3) the proposal of a loss function combining Powerful-IoU v2 (PIoU v2) and Normalized Wasserstein Distance (NWD) weighted averaging, where PIoU v2 focuses on optimizing overlapping regions, and NWD targets small object optimization. The combined loss function enables comprehensive optimization of the bounding boxes, improving the model's accuracy and convergence speed. Experimental results show that on the UAV-PDD2023 dataset, Pavement-DETR improves the mean average precision (mAP) by 7.7% at IoU = 0.5, increases mAP by 8.9% at IoU = 0.5-0.95, and improves F1 Score by 7%. These results demonstrate that Pavement-DETR exhibits better performance in road defect detection, making it highly significant for road maintenance work.
准确检测道路缺陷对于提高道路维护的安全性和效率至关重要。本研究聚焦于六种常见的路面缺陷类型：横向裂缝、纵向裂缝、块状裂缝、斜向裂缝、坑洼和修复痕迹。在实际场景中，关键挑战包括有效区分前景和背景，以及准确识别小尺寸（如细裂缝、密集块状裂缝和聚集坑洼）和重叠缺陷（如相交裂缝或多个缺陷紧密相邻出现的聚集损坏区域）。为解决这些问题，本文提出一种基于实时检测变压器（RT-DETR）的路面缺陷检测模型（Pavement-DETR），旨在优化缺陷检测的整体准确率。为实现这一目标，提出了三项主要改进：（1）在ResNet骨干网络的第三（S3）和第四（S4）阶段引入通道空间混洗（CSS）注意力机制，这两个阶段分别对应中级和高级特征层，使模型能够更精确地聚焦于道路缺陷特征；（2）采用Conv3XC结构进行特征融合，通过多级卷积、通道扩展和跳跃连接增强了模型区分前景和背景的能力，这也有助于改善梯度流和训练稳定性；（3）提出一种结合强大交并比v2（PIoU v2）和归一化瓦瑟斯坦距离（NWD）加权平均的损失函数，其中PIoU v2专注于优化重叠区域，而NWD旨在优化小目标。组合损失函数能够对边界框进行全面优化，提高模型的准确率和收敛速度。实验结果表明，在无人机路面缺陷数据集（UAV-PDD2023）上，Pavement-DETR在交并比（IoU）=0.5时将平均精度均值（mAP）提高了7.7%，在IoU = 0.5-0.95时mAP提高了8.9%，F1分数提高了7%。这些结果表明，Pavement-DETR在道路缺陷检测中表现出更好的性能，对道路维护工作具有重要意义。
