# Traffic Light and Arrow Signal Recognition Based on a Unified Network
## 4 Results
### Evaluation  
The mean average precision (mAP) score was used to evaluate the object detection results. The daytime images in the LISA dataset were utilized for training and testing to compare our approach with previous methods, as indicated in Table 1. The intersection over union (IoU) was set as 0.5. From the results, conventional detectors did not perform well because there were complicated scenes. In Table 1, the results reported in row 1 (color detector), row 2 (spot detector), and row 3 (aggregate channel features detector, ACF detector) were extracted from [PERSON] et al. [2], and those reported in row 4 (Faster R-CNN), row 5 (spot light detection), row 6 (modified ACF detector), and row 7 (multi-detector)  
Figure 8: Examples in the LISA dataset. (**a**) Traffic scene, and (**b**) traffic lights. Figure 9: Examples in our dataset. (**a**) The traffic light images obtained with 3.5/12 mm lens cameras, and (**b**) traffic lights. were extracted from [PERSON] et al. [24]. The low accuracy of Faster R-CNN can primarily be attributed to the small regions of traffic lights, which were hard to detect after performing convolution layer-by-layer [24]. The proposed approach (results shown in the last row, Table 1) outperformed the other methods for both circular lights and arrow lights. Table 2 presents the accuracy and computation speed results of multiple network structures when they were applied to our dataset. Four network structures (including one with data augmentation) were compared. The first network structure used YOLOv3 to detect the traffic lights and AlexNet [25] to classify light states. The second network structure used the combined YOLOv3 + YOLOv3-tiny + LeNet approach. However, in this case, the three networks operated as independent networks. The unified network structure integrated these three subnets. These networks were trained and tested with the same dataset. Table 2 shows that the proposed methods achieved higher mAPs than that of the YOLOv3 + AlexNet network structure but required more computation costs. Relative to the first two networks, the one using LeNet for arrow light classification had a higher mAP but required more computation costs. As seen from Table 2, the two versions of the proposed unified network (the second-rightmost and rightmost columns in Table 2) had higher mAPs than the YOLOv3 + YOLOv3-tiny + LeNet network structure did. Furthermore, their computation speed was quicker because of sharing feature maps. Images captured with varying distances contained traffic lights with varying sizes of ROIs. This appeared to affect the results of traffic light detection and recognition. In the proposed approach, 3.5 and 12 mm lens cameras were used for capturing images. Table 3 shows the mAP for varying ROI sizes of traffic lights (height measured in pixels), the size of traffic lights at varying distances, and the mAP for varying distances. The traffic light images captured by the 12 mm lens camera were bigger than those taken by the 3.5 mm lens camera. The images with bigger traffic lights provided better results for detection. The detection results for the images captured by the 12 mm lens camera were better than those for the images captured by the 3.5 mm lens camera for different distances. Nevertheless, we used the 3.5 mm lens camera for near scenes. At the distance of 0 to 15 m, traffic light images could only be taken by the 3.5 mm lens camera because of the cameras' field of view (see Figure 10 as an illustration).