Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 210096, China.
School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798, Singapore.
Sensors (Basel). 2023 May 28;23(11):5138. doi: 10.3390/s23115138.
Bridge deck pavement damage has a significant effect on the driving safety and long-term durability of bridges. To achieve the damage detection and localization of bridge deck pavement, a three-stage detection method based on the you-only-look-once version 7 (YOLOv7) network and the revised LaneNet was proposed in this study. In stage 1, the Road Damage Dataset 202 (RDD2022) is preprocessed and adopted to train the YOLOv7 model, and five classes of damage were obtained. In stage 2, the LaneNet network was pruned to retain the semantic segmentation part, with the VGG16 network as an encoder to generate lane line binary images. In stage 3, the lane line binary images were post-processed by a proposed image processing algorithm to obtain the lane area. Based on the damage coordinates from stage 1, the final pavement damage classes and lane localization were obtained. The proposed method was compared and analyzed in the RDD2022 dataset, and was applied on the Fourth Nanjing Yangtze River Bridge in China. The results shows that the mean average precision (mAP) of YOLOv7 on the preprocessed RDD2022 dataset reaches 0.663, higher than that of other models in the YOLO series. The accuracy of the lane localization of the revised LaneNet is 0.933, higher than that of instance segmentation, 0.856. Meanwhile, the inference speed of the revised LaneNet is 12.3 frames per second (FPS) on NVIDIA GeForce RTX 3090, higher than that of instance segmentation 6.53 FPS. The proposed method can provide a reference for the maintenance of bridge deck pavement.
桥面铺装层损坏会对桥梁的行车安全和长期耐久性产生重大影响。为实现桥面铺装层的损伤检测和定位，本研究提出了一种基于 YOLOv7 网络和改进版 LaneNet 的三阶段检测方法。在第 1 阶段，对 Road Damage Dataset 202（RDD2022）进行预处理并用于训练 YOLOv7 模型，得到了 5 类损伤。在第 2 阶段，对 LaneNet 网络进行剪枝，保留语义分割部分，使用 VGG16 网络作为编码器生成车道线二值图像。在第 3 阶段，通过提出的图像处理算法对车道线二值图像进行后处理，得到车道区域。基于第 1 阶段的损伤坐标，最终得到了路面损伤类别和车道定位。在 RDD2022 数据集上对提出的方法进行了对比分析，并应用于中国南京第四长江大桥。结果表明，预处理后的 RDD2022 数据集上 YOLOv7 的平均准确率（mAP）达到 0.663，高于 YOLO 系列中的其他模型。改进版 LaneNet 的车道定位准确率为 0.933，高于实例分割的 0.856。同时，改进版 LaneNet 在 NVIDIA GeForce RTX 3090 上的推理速度为 12.3 帧每秒（FPS），高于实例分割的 6.53 FPS。该方法可为桥面铺装层的维护提供参考。
