<![CDATA[This study focuses on developing an algorithm that accurately calculates the volume of vehicles passing through a busy crossroads in Indonesia using object recognition. The high density of vehicles and their proximity often pose a challenge when distinguishing between vehicle types using a camera. Therefore, the proposed algorithm is designed to assign a unique identity (ID) to each vehicle and other objects, such as pedestrians, ensuring that volume calculations are not repeated. The objective is to provide an equitable comparison of road density and the total number of detected vehicles, enabling the determination of whether the road is crowded. To accomplish this, the algorithm incorporates the Non-Max Suppression function, which displays bounding boxes around objects with confidence values and counts the objects within each box. Even when objects are nearby, the algorithm tracks them effectively, thanks to the support of the Darknet Algorithm. The main capabilities of this algorithm for improving vehicle detection include enhanced accuracy, speed, and generalization ability. Typically, it is used in conjunction with the You Only Look Once (YOLO) object detection framework. Five convolutional neural network models are tested to assess the algorithm's accuracy: YOLOv3, YOLOv4, CrResNext50, DenseNet201-YOLOv4, and YOLOv7-tiny. The training process utilizes the Darknet Algorithm. The best-performing models, YOLOv3 and YOLOv4, achieve exceptional accuracy and F1 scores of up to 99%. They are followed by CrResNext50 and DenseNet201-YOLOv4, which achieve accuracy rates of 92% and 98% and F1 scores of 94% and 98%, respectively. The YOLOv7-tiny model achieves an accuracy rate and F1 score of 86% and 88%, respectively. Overall, the results demonstrate the algorithm's success in accurately detecting and calculating the volume of vehicles and other objects in a busy intersection. This makes it a valuable tool for regional government decision-making.]]>
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