# Tire Defect Detection via 3D Laser Scanning Technology
## 3 Experiments and Discussion
### Evaluation on the 3D Tire Dataset  
In this section, we evaluated several state-of-the-art 2D detectors, including our model, on three tire defect datasets. In the development process of object detection, three mainstream methods have gradually emerged: anchor-based methods (Faster-RCNN [31]), anchor-free methods (FCOS [21] and RetinaNet [32]), and DETR-based methods (Deformable-DETR [8], DAB-DETR [33], and Conditional DETR [34]). Our proposed detector adopted the most advanced DETR-based architecture. The evaluation results are shown as follows. * **Implementation details** We used ImageNet [35] and pretrained ResNet-50 [36] as the backbone for all methods. For a fair comparison, all methods shared the same optimizer, scheduler, and epoch. The shared optimizer was an Adam optimizer [37] with a learning rate of \(2\times 10^{-4}\), \(\beta_{1}=0.9\), and \(\beta_{2}=0.999\) and a weight decay of \(10^{-4}\). All models were trained for 100 epochs; the  
Figure 6: Visual comparison of previous defect-detection methods for tire bubble defects from the 3D dataset. Red frames indicate the defects. We show that existing X-ray-based approaches are insensitive to this type of defect because of its low visual quality. learning rate decayed at the 40 th, 60 th, and 80 th epochs by a factor of 0.1. All experiments were conducted in 4 NVIDIA GEFORCE 1080 Ti graphic cards. * **Experiment on RGB images** We first conducted experiments on captured images using RGB cameras. As shown in Table 1, our 2D detector outperformed state-of-the-art methods by a large margin. Compared with DAB-DETR [33], which achieved the second-best mAP, our detector improved mAP by 10.6%. However, because of the aforementioned drawbacks of 2D images, detecting such defects by human eyes is intrinsically difficult. Therefore, all detection methods performed unsatisfactorily, and all methods particularly failed to detect very tiny defects as AP\({}_{\text{S}}\) was 0. Anchor-free based FCOS [21] and RetinaNet [32] failed on this dataset. The evaluation results here indicate that photoed images are unsuitable for the detection of tiny defects. * **Experiment on rendered images** We then experimented with rendered images. The quantitative results are shown in Table 2 in detail. Apparently, our method outperformed the other techniques by a large margin; specifically, our method achieved 0.691 mAP, which was 16.7% better than that of Faster R-CNN [31]. For defects with large sizes and medium sizes, our method achieved 0.896 AP\({}_{\text{L}}\) and 0.592 AP\({}_{\text{M}}\). In particular, for tiny defects, our method achieved 0.357 AP\({}_{\text{S}}\), which was 51.3% better than that of the second-ranked Faster-RCNN, showing that our method is sensitive to tiny defects and promising in practical applications. The qualitative results are illustrated in Figure 7. For each method, we showed the bounding box with the highest confidence, which was colored in red. Many normal areas had similar patterns with defective areas, which confused the detection. For example, RetinaNet [32] identified the black burrs as defects in the first row. Faster-RCNN [31] recognized the normal bulge as a defective one. Thanks to several novel designs, our detector accurately detected the real defects, whereas other competitors failed in some cases. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **mAP** & **AP\({}_{\text{50}}\)** & **AP\({}_{\text{75}}\)** & **AP\({}_{\text{S}}\)** & **AP\({}_{\text{M}}\)** & **AP\({}_{\text{L}}\)** \\ \hline Faster R-CNN [31] & 21.0 & 56.0 & 13.2 & 0.0 & 12.2 & 29.1 \\ FCOS [21] & 1.9 & 8.8 & 0.6 & 0.0 & 0.0 & 5.5 \\ RetinaNet [32] & 1.7 & 10.1 & 1.2 & 0.0 & 0.9 & 4.1 \\ Deformable-DETR [8] & 25.2 & 58.8 & 14.8 & 0.0 & 15.6 & 31.3 \\ Conditional DETR [34] & 25.1 & 59.2 & 13.2 & 0.0 & 15.8 & 32.0 \\ DAB-DETR [33] & 32.2 & 61.2 & 15.5 & 0.0 & 18.2 & **32.5** \\ \hline Ours & **35.6** & **62.1** & **17.4** & 0.0 & **21.0** & **32.5** \\ \hline \hline \end{tabular}
\end{table}