<![CDATA[The importance of traffic density measurement in road planning has led to efforts in automation using object detection algorithms, particularly YOLO (You Only Look Once), which are replacing error-prone and time-consuming manual processes. However, challenges arise in dense traffic conditions, posing a challenge to vehicle detection accuracy. This research aims to compare the performance of vehicle detection between two YOLO approaches: multi-view layer detection and conventional detection, focusing on YOLOv5n, YOLOv5s, and YOLOv5m. The literature review encompasses Computer Vision, YOLO implementation, and related research to provide conceptual context. The research method details the steps of vehicle identification using YOLOv5, and the evaluation includes the performance of various YOLO variants and multi-view detection approaches. Thus, this study is expected to gain deeper insights into building an effective model and facilitating the selection of a suitable YOLO model for vehicle detection.]]>
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