Automatic Detection of Urban Pavement Distress and Dropped Objects with a Comprehensive Dataset Collected via Smartphone

Abstract

Pavement distress seriously affects the quality of pavement and reduces driving comfort and safety. The dropped objects from vehicles have increased the risks of traffic accidents. Therefore, automatic detection of urban pavement distress and dropped objects is an effective method to timely evaluate pavement condition. Firstly, this paper utilized a portable platform to collect pavement distress and dropped objects to establish a high-quality dataset. Six types of pavement distresses: transverse crack, longitudinal crack, alligator crack, oblique crack, potholes, and repair, and three types of dropped objects: plastic bottle, metal bottle, and tetra pak were included in this comprehensive dataset. Secondly, the real-time YOLO series detection models were used to classify and localize the pavement distresses and dropped objects. In addition, segmentation models W-segnet, U-Net, and SegNet were utilized to achieve pixel-level detection of pavement distress and dropped objects. The results show that YOLOv8 outperformed YOLOv5 and YOLOv7 with a MAP of 0.889. W-segnet showed an overall MIoU of 70.65% and 68.33% on the training set and test set, respectively, being superior to the comparison model and being able to achieve high-precision pixel-level segmentation. Finally, the trained models were performed on the holdout dataset for the generalization test. The proposed methods integrated the detection of urban pavement distress and dropped objects, which could significantly contribute to driving safety.

Keywords

• pavement distress
• dropped objects
• object detection
• semantic segmentation