Performance One Stage Layer Object Detection Model to Count People using Various Backbones

The development of computer vision involving artificial intelligence has been applied in several sectors. People counting is not only a tool for measuring the number of people but also a key element in security management, compliance, and operational efficiency, while creating a safe and enjoyable environment in public areas. One of them is about single-stage layer-based people counting using You Look Only Once (Yolo) and Single Shoot Detector (SSD). To implement object detection, a backbone for feature extraction is required. In this article, Yolo uses v7 with an Extended Efficient Layer Aggregation Network (ELAN) backbone, while for SSD it uses a mobilenet, inception, and resnet backbone. Data for testing was obtained by taking pictures in one of the public areas, namely at a train station in Indonesia. Evaluation uses average precision (AP) and calculates the accuracy of the number of people. The number of people is calculated based on three categories, small conditions (1-15 people), medium conditions (16-25 people), and many more than 26 people (crowded condition). Based on these results, the most significant value was obtained at 0.985 for YoloV7. The highest accuracy was obtained with a result of 100% for YoloV7 in a small number of conditions (1-15 people).