Implementation of YOLO v7 algorithm in estimating traffic flow in Malang

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The escalating prevalence of traffic congestion in Malang, which has worsened over time, has resulted in increased inconvenience and traffic-related issues. As a solution, we propose the development of an intelligent transportation system based on traffic flow estimation. While this endeavor presents challenges, advancements in traffic data accessibility and deep learning algorithms, including You Only Look Once (YOLO), R-CNN based ResNet101, Inception V2, and others, with YOLOv7 demonstrating superior performance, continue to enhance traffic flow estimation. Consequently, this study aims to describe and assess the YOLOv7 model's suitability for estimating traffic flow. To achieve this, we propose a traffic monitoring system utilizing the YOLOv7 model in conjunction with a SORT filter, capable of tracking vehicle count and average speed using a custom dataset comprising car images and CCTV footage of Malang's traffic. Subsequently, the number and average speed of cars passing on the road were computed by delineating two virtual boxes. According to the findings, our YOLOv7 system exhibited higher average precision, achieving 61.3%, 78.6%, and 62.1% for each test, and boasted a faster average inference time of 90.67 seconds, meeting the requirements of an intelligent transportation system. Nevertheless, with an average percent deviation in vehicle count exceeding 30% compared to the pre-trained model, vehicle detection is suboptimal, consequently impacting average vehicle speed detection adversely. Hence, our custom model necessitates further optimization and training before practical implementation in real-world traffic estimation.