# Implementation of Pavement Defect Detection System on Edge Computing Platform
## 5 Experimental Results and Quantitative Analysis  
To prove the effectiveness of the proposed detection strategy, experiments were performed on our established Taiwan pavement defect image dataset (TPDID), which is the first dataset for Taiwan pavement defects (Figure 6). TPDID has 62,000 samples, and pavement defects are divided into six categories, namely Category 0: road potholes and sudden slopes (31,708 samples), Category 1: manhole covers (9284 samples), Category  
Figure 4: Improvement effect of model compression ratio. Figure 5: Implementation of embedded system for YOLOv3-based pavement defect detection systems. (**a**) Xilinx ZCU104 platform; (**b**) Nvidia TX2 platform; (**c**) quantitative performance comparison. 2: longitudinal cracks (16,810 samples), Category 3: transverse cracks (4761 samples), Category 4: longitudinal patches (18,023 samples), and Category 5: transverse patches (10,291 samples). The lenses used for this database were RGB three-channel lenses that complied with vehicle application regulations. In this study, two lenses with different FoVs were used to achieve efficient recognition at short, medium, and long distances; this strategy can efficiently increase the detection accuracy rate, decrease the misjudgment rate, and lengthen the recognition distance. Figure 7 shows the results of pavement defect detection using the YOLOv3 model. The images of the road pavement captured via the 30\({}^{\circ}\) FoV and 70\({}^{\circ}\) FoV lenses are on the left and right sides of Figure 7, respectively. The top two images in Figure 7a,b show the detection results of road pavement defects at time step \(t\). The image below shows the detection and tracking results at time step \(t+j\). As the left side of Figure 7 indicates that 30\({}^{\circ}\) FoV has a focal length in the distance due to the small angle of view, the pavement defects in the far FoV are large and clear. The right side of 70\({}^{\circ}\) FoV has a broader viewing angle, wider viewing field, and closer focal length, making it suitable for detecting medium and short distance objects. The pink frame in Figure 7 is the identified pothole category, the red frame is the manhole cover category, and the green frame is the longitudinal crack. Alongside this, we can observe from the upper left of Figure 7a that when the road pavement defect is far away from the vehicle, it can be identified by 30\({}^{\circ}\) FoV. By contrast, no detection results are available via 70\({}^{\circ}\) FoV as shown in the upper right of Figure 7a. With this in mind, the detection results of 30\({}^{\circ}\) FoV are preferable when smaller pavement defects are found; moreover, if the distance between the pavement defects and the vehicle is decreasing, the detection results of 70\({}^{\circ}\) FoV are preferred to avoid incomplete pavement defects. In addition, since the features of the road potholes are not noticeable in Figure 7b, no recognition results are obtained from the images of the two distinct FoV perspectives at time step \(t\) (no pink prediction frame at the top of Figure 7b). Conversely, at time step \(t+j\), when the distance between the pothole and the vehicle is decreasing (the two lower images in Figure 7b), both images identify the pothole. Thus, the approach presented in this study not only effectively extends the recognition distance of pavement defects using two different FoV lenses but also contributes to effectively increase the detection accuracy rate and decrease the miss rate. Meanwhile, a tracking algorithm was employed in this study to improve the reliability of the detection system. Figure 6: Taiwan pavement defect image database (TPDID). Figure 8 shows the loss function and mean AP (mAP) of the neural network training process of YOLOv3 for the proposed pavement defect detection system. As seen in Figure 8, we consider the network model parameter set at the maximum mAP (approximately 70.2415%) as the network model applied for testing.