As an additive to improve the performance of asphalt binder, tire pyrolysis carbon black is gradually being used in road engineering, but the effect of carbon black (CB) with different particle sizes on asphalt modification remains to be further studied. In this study, three kinds of particle sizes and three kinds of contents of CB were used to modify asphalt, and different tests were conducted to research the high temperature performance and fatigue resistance of carbon black modified asphalt binder. It is found that the addition of CB can enhance the rutting resistance and medium temperature fatigue performance of virgin asphalt binder in general. However, for CB of 270 μm and 2.6 μm, its addition under certain contents lead to the decrease of high temperature performance and fatigue performance of the asphalt binder. For aged asphalt, the addition of CB decreases the rutting resistance and improves the fatigue resistance. The recommended content and particle size of CB are 2% and 2.6 μm. This study refines the complex effects of CB on asphalt properties, providing a reference for determining the size and content of CB in asphalt modification.

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.