# Implementation of Pavement Defect Detection System on Edge Computing Platform
## 5 Experimental Results and Quantitative Analysis
Figure 9 demonstrates the quantitative analysis for six pavement defect categories using the YOLOv3 model. The left-hand side of Figure 9 indicates the average detection accuracy of each category, and the right-hand side of Figure 9 shows the miss rate of each category. Notably, when the IOU threshold value >0.5, its pothole category average precision is about 71%, and the miss rate is 29%, according to the quantification result. The best detection results were obtained from Category 1, which had an average precision of 89% and a miss rate of 17%. This is primarily because there is a considerable amount of manhole covers on the roads of Taiwan, and the features are very distinctive, which is why the detection performance of this category is the best. Figure 7: Pavement defect detection results by two lenses with different viewing angles. (**a**) Detection results at time step t; (**b**) Detection and tracking results at time step \(t+j\). Figure 10 shows the pavement defect detection results for Category 0 under a low-illumination night environment via a car camera. Since an average car camera has a high dynamic range characteristic, it can be seen from Figure 10 that the output image looks like a grayscale image. Although the image is no color information, it can effectively reduce the noise for the camera in low-light conditions and further improve the ability to identify objects at night. As shown in Figure 10, it can still give a great pothole recognition effect in a low-light environment at night. Figure 8: Loss function and mAP for our proposed pavement defect detection system. Figure 9: Average precision and miss rate for 6 pavement defect categories. (**a**) Average detection accuracy of each category; (**b**) Miss rate of each category. To verify the robustness of the proposed road defect detection system, an extensive video recorded on the Pacific Northwest highway [36], which includes the potholes and patches of snow and water, was used. The experimental results show that the proposed system can effectively recognize potholes under severe conditions, such as water surface reflection interference or potholes with complex structures (see Figure 11 below). The experimental results can be visualized via videos that can be found at the following link: [[https://youtu.be/_6](https://youtu.be/_6) OhNANpTWg]([https://youtu.be/_6](https://youtu.be/_6) OhNANpTWg) (accessed on 4 February 2021). We also conducted the quantitative analysis and comparison of prominent PC-based DL object recognition models, such as YOLOv4, MobileNet-YOLO, TF-YOLO, and RetinaNet, for pavement defect detection. Figure 12 shows the detection results for different models for pavement defects, where panels a-d show the detection results of YOLOv4, MobileNet-YOLO, TF-YOLO, and RetinaNet, respectively. YOLOv4 had good detection results, whereas MobileNet-YOLO and TF-YOLO had poor detection results for 30\({}^{\circ}\) FoV, especially TF- YOLO, and the recognition qualities were not good with both FoVs. Lastly, RetinaNet could also identify some defects, but some defects were not identified. Figure 11: Pothole recognition results under severe conditions from the Pacific Northwest highway dataset. Figure 10: Pothole recognition results in low-illumination night environment. Further, we conducted a quantitative comparison and analysis of mAP and FPS for each of the abovementioned models (Figure 13). From the figure, except for TF-YOLO, whose computational speed attained roughly 27 FPS, all other models reached over 30 FPS, which fulfilled the demand of real-time vehicle applications. MobileNet-YOLO had the fastest computation speed, but the average detection accuracy rate was relatively low; meanwhile, YOLOv4 not only had a higher detection score but also fulfilled the demands of real-time operation. In consideration of both mAP and computing speed, we adopted the YOLOv3 model with the KF tracking algorithm as the road pavement defect detection algorithm and implemented the system via Xilinx embedded hardware platform.