## 2 Related Work
### Research Study of Vehicle Detection  
Over the years, a series of improvements and advancements in object detection research have increased the prediction of objects with a high accuracy rate of frames per second. The object detection approach is categorized into two methods: the two-stage and one-stage method. The two-stage approach based on old research gives high prediction accuracy but cannot achieve speed and higher performance [5]. The two-stage category networks to detect objects are Region-Based Convolutional Neural Network (R-CNN), Fast R-CNN, Faster R-CNN, and Spatial Pyramid Pooling Network (SPP-net) [23]. This network system builds up object proposals for a group and then gives predictions based on object regions and corresponding levels. On the other hand, one one-stage approach has better ongoing execution exactness and speed on MS COCO and PASCAL VOC datasets [24; 25]. Various two-stage object detection algorithms have been proposed, like R-CNN, Fast R-CNN, Faster R-CNN, and Mask R-CNN, for the necessity of speed and accuracy [12; 13; 14; 15]. [PERSON] et al. [21] proposed a two-stage SPP-net. The CNN was 20 times quicker than R-CNN [13] with similar accuracy. It can also obtain inputs of non-fixed size, disregarding the size of the region of interest. [PERSON] et al. [12] proposed a region proposal network (RPN) based on Fast R-CNN and Faster R-CNN, resulting in an improved object detection algorithm using candidate box generation. In 2017, Mask R-CNN was proposed. It comprises RPN, feature pyramid network (FPN), and detection. It can perform three tasks, namely target recognition [26], detection [27], and segmentation [28]. The drawbacks of this two-stage approach include its speed because of first-stage localization and identification of objects in the second stage. Various object detection algorithms have been proposed based on the one-stage approach. They are fast and make predictions of objects with high efficiency [5]. [PERSON] et al. [29] proposed a YOLO model in 2016 that is state-of-the-art in real-time object detection. Regression techniques divide input images into S \(\times\) S grids for the estimation of bounding boxes and the classification probability of the detected objects; various techniques have been employed. [1]. You Only Look Once Version 5 needs one complete pass to generate the bounding box using the convolutional neural network. Therefore, this model is commonly used for object detection because of its higher real-time detection rate. Subsequently, the author introduced the improved version of YOLO in 2017, called YOLOv2, with higher accuracy. This improvement was achieved using batch normalization in the CNN, the classifier with high resolution, Darknet19 network, and anchor boxes to predict the object location [9]. Then, in 2018, the author introduced YOLOv3 with some improvements, further improving the accuracy rate and speed. This version replaced the Darknet-19 with Darknet-53 to be more effective than previous versions [10]. In early 2020, [PERSON] et al. [30] introduced an advanced version of YOLOv3 called YOLOv4. It turned out to be more accurate and faster by using Cross Stage Partial Network (CSP Darknet-53), a combination of Darknet53 and the CSP-Net [30]. More importantly, this model was intended to empower training on a conventional GPU, contrary to alternative models [29], as well as incorporated many features such as CSP connections along with the CSP Darknet-53, the activation function of Mish and Leaky ReLU [31; 32; 33], the adoption of Path Aggregation Network (PANet) by replacing the FPN used in YOLOv3 and Spatial Pyramid Pooling [34] in order to achieve the best efficiency and higher accuracy for object detection. [PERSON] introduced a YOLOv5 model with several improvements for achieving the high detection accuracy and speed. This model is the most recent variant of the YOLO series [35].