# A Review on Automated Detection and Identification Algorithms for Highway Pavement Distress
## 4 Evolution of Pavement Image Processing Techniques
### Deep Learning Approaches
#### 4.3.2 Object Detection  
Object detection provides critical advantages for pavement distress analysis by automatically localizing distress areas with bounding boxes, overcoming the limitations of simple CNN classification. This capability enables simultaneous distress identification and severity assessment, significantly advancing automated pavement condition evaluation. Currently, several comprehensive pavement condition analysis methods based on object detection have emerged. For instance, [PERSON] et al. [42] combined object detection with Pavement Condition Index (PCI) computation and visualization, while [PERSON] et al. [43] developed 3D object detection networks incorporating BIM (Building Information Modeling) technology for visualized distress management. These research advances collectively provide promising technical support for intelligent engineering applications. Deep learning-based object detection primarily adopts two-stage or one-stage frameworks. Two-stage object detection algorithms, primarily represented by region-based convolutional neural networks (RCNNs) [44; 45; 46; 47], consist of two sequential steps: region proposal generation followed by classification and regression. This architecture generally achieves higher accuracy compared to one-stage methods. For instance, the RCNN [44] first generates \(\sim\)2000 candidate regions using selective search (SS) and then processes each region through a CNN for feature extraction and classification (Figure 6). One-stage detectors such as SSD (Single-Shot MultiBox Detector) [48] and YOLO (You Only Look Once) [49] integrate localization and classification into a single network pass, optimizing for real-time processing. As illustrated in Figure 7, YOLO divides input images into grid cells, each predicting bounding boxes and class probabilities simultaneously, with non-maximum suppression (NMS) removing duplicate detections. This efficient architecture achieves faster inference than two-stage methods, though with slightly lower accuracy in complex cases. In a study by [PERSON] et al. [50], the performance of YOLOv5s and Faster RCNN was evaluated using 383 test images. The results showed that YOLOv5s completed object detection on all images in approximately 1.71 min, significantly faster than Faster RCNN, which took around 15 min. However, in terms of detection accuracy, Faster RCNN achieved a higher mean Average Precision (mAP) of 0.795, compared to YOLOv5s' mAP of 0.683. Table 2 provides a comparative analysis of contemporary object detection algorithms for road distress recognition, including key performance metrics such as training datasets, target objects, and mAP scores. Figure 6: RCNN network flowchart. Based on the analysis of the aforementioned table, pavement distress detection has evolved from single-defect identification to comprehensive systems capable of detecting multiple complex distress types simultaneously, including transverse cracks, longitudinal cracks, alligator cracking, rutting, and construction joint defects. Modern data collection employs multiple devices (e.g., smartphones, UAVs, vehicle cameras) combined with multi-angle imaging and augmentation techniques to ensure dataset diversity. Algorithm improvements focus on three key aspects: (1) backbone network optimization, (2) advanced feature fusion, and (3) enhanced training strategies using attention mechanisms and activation function refinements.