## 2 Related Work
### Studies Related to the Detection of Objects Other Than Traffic Signs  
[PERSON] and [PERSON] [41] performed vehicle detection using the CNN, k-nearest neighbor (KNN), Haar cascade, and YOLO algorithms. The accuracy of YOLO based on five different video tests was 93%, which was higher than that for the CNN, KNN, and Haar cascade (accuracies of 59%, 89%, and 59%, respectively). This highlights YOLO's superior performance in vehicle detection. He [42] compared the performances of Faster R-CNN, SSD, and YOLOv4 using the same road settings and found that the mAP of each model was 73.5%, 81.5%, and 87.2%, respectively. This demonstrates that YOLOv4 had a higher efficiency than the other two models. [PERSON] and [PERSON] [43] presented a high-precision YOLOv4-based model for vehicle detection. The vehicle width and height were determined by deepening the CNN and combining the anchor mechanism with the K-means++ algorithm. Feature fusion was performed using FPN+PAN. Lastly, the complete intersection over union was used as the loss function for the coordinate prediction. The results showed that the mAP of the new model was higher than that of the original YOLOv4 by 3%. [PERSON] et al. [44] developed an improved YOLOv4-based video stream vehicle target detection method that resolved the problem of slow detection speeds. The results revealed that the FPS of the improved algorithm was about six units higher than the original model, and the detection outcomes were not lowered when the improved model was used in conjunction with the Camshift tracking algorithm. [PERSON] et al. [45] applied TensorRT, MobileNetv3, and the channel-pruning method to improve the detection speed of YOLOv4. [PERSON] and [PERSON] [46] proposed a YOLOv4-based model that improved the detection speed and accuracy through the use of the MobileNetv1 network. The 13 \(\times\) 13 prediction frame was replaced with a 104 \(\times\) 104 prediction frame to increase the precision of small target detection. Meanwhile, the K-means algorithm was used in cluster analysis to generate the anchor box of the network. The results showed that the mAP of the new model was 90.32% at 35 FPS. Even though the mAP was 2.66% less than the original YOLOv4, the model size was only 23.70% of YOLOv4, and the detection speed was 1.66 times higher. [PERSON] et al. [47] proposed the YOLOv5-Ghost model, which involved modifying the network architecture of YOLOv5, thus lowering the computational complexity. The mAP of YOLOv5 was 83.36% at 28.57 FPS, whereas the mAP of YOLOv5-Ghost was 80.76% at 47.62 FPS. This shows that the latter is more suitable for being placed in embedded devices. [PERSON] et al. [19] proposed a YOLOv5-based automatic driver and driver assistance detection system for detecting traffic signs, vehicles, and pedestrians. The system was tested on three embedded platforms(Jetson Xavier AGX, Jetson Xavier Nx, and Jetson Nano), with the results showing that Jetson Xavier AGX achieved the fastest detection and highest detection precision. [PERSON] et al. [48] analyzed and tested the influence of video quality on target detection in self-driving scenarios. Gaussian noise and motion blur were added to the videos to simulate foggy, cloudy, and rainy environments. [PERSON] et al. [49] proposed an improved YOLOv4-Tiny-based method for detecting targets under poor weather conditions. The model increased the recognition of small targets in street scenarios and was more robust under both rainy and foggy conditions, with a mAP of 68.88% compared to the 64.75% of the original YOLOv4-Tiny. [PERSON] and [PERSON] [50] reviewed the current developments in obstacle detection systems in automatic vehicles, determining that a combination of different obstacle detection techniques (such as radar, vision cameras, ultrasonic sensors, and infrared camera monitoring) achieved a better representation of driving environments. [PERSON] et al.