## 2 Related Work
### Studies Related to the Detection of Objects Other Than Traffic Signs
[51] used an unmanned aerial vehicle camera alongside a proprietary MATLAB algorithm combined with YOLOv4 to detect accident sites and the extent of vehicle damage. The level of traffic congestion was determined based on the pixels of the moving vehicles. [PERSON] et al. [52] proposed an improved algorithm that integrated the PReNet and YOLOv4 networks, which effectively reduced additional convolutional layers and resolved the problem of unideal small target detection results caused by higher network layers. When used alongside the K-means algorithm, the model was able to allocate the targets in different feature maps to more suitable view frames in multiscale detection. Compared to the original YOLOv4, the improved algorithm had a 29.74% higher mAP and 16.26 higher FPS. [PERSON] et al. [53] presented a YOLOv3-based algorithm that used the Retinex image enhancement algorithm to augment the image quality and resolve the issues in vehicle collision detection during poor weather. The model was also able to rapidly detect collision types in mixed traffic flow settings; the detection rate of the proposed algorithm was 92.5%. [PERSON] et al. [54] presented a traffic light detection method, using YOLOv5 as its algorithmic core to generate anchors through K-means. Using a traffic light dataset, the mAP achieved by the model was 63.3% at a detection speed of 143 FPS. [PERSON] et al. [55] proposed a traffic offense detection system based on YOLOv4 and YOLOv4-Tiny. The former was used for vehicle detection and had an accuracy of 86%, and the latter was used for helmet detection and had an accuracy of 92%. The DeepSORT algorithm was used to track vehicles though camera modules. [PERSON] and [PERSON] [56] developed an intelligent vehicle lane detection system that could be used for various road conditions. The images were preprocessed by gray-scaling the images using the weighted-average method and with binarization using the Otsu algorithm. The Canny edge detection algorithm was used as the edge extraction operator. Detection of straight and curved lanes was accomplished using an improved Hough transform and the least-squares method. The detection accuracies for after rain, congested lane, and road surface conditions were 97.715%, 96.313%, and 94.611%, respectively. [PERSON] and [PERSON] [57] also proposed a method for lane detection in which computer vision was integrated with the Sobel algorithm, thus resolving the error when switching lanes, with YOLO also used to recognize vehicles and other obstacles to assist drivers in their decision making. [PERSON] et al. [58] proposed a lane-marking detector that used CNNs to capture and record lane-marking features while reducing the system complexity and maintaining a high precision. The model achieved a 65.2% mean intersection-over-union accuracy at a detection speed of 34.4 FPS on the CamVid dataset. [PERSON] et al. [59] focused on improving the dashcam storage space and object recognition rate. The experiments revealed that the compressed sensing method of the iterative shrinkage thresholding algorithm with the network was able to reduce the storage space by 60% while maintaining image resolution. In addition, YOLOv4 was able to overcome complex environments by achieving a minimum 80% recognition rate in 480 \(\times\) 480 pixels. [PERSON] and [PERSON] [60] proposed a YOLOv3-based deep-learning model integrated with Canny edge detection to detect and classify highway accidents, and after that, [PERSON] and [PERSON] [61] proposed a Mask R-CNN-based model integrated with the Retinex image enhancement algorithm for detecting rockfalls and potholes ahead of vehicles traveling on hill roads.