Moving Object Detection in Video with Algorithms YOLO and Faster R-CNN in Different Conditions

Abstract

In this study, YOLOv4 with YOLOv3 and Faster R-CNN compared for object detection both under challenging weather conditions and in the dark. It is difficult to detect moving objects such as pedestrians, cars, buses, and motorcycles in bad weather conditions or at night, especially in adverse weather conditions such as rain, fog, and snow. The objective of this study is to assess the performance of these three algorithms in such circumstances, as none of them were designed to work in bad weather or at night. Tesla P4 GPUs with 12GB of RAM were used for this study, with algorithms trained using open-image datasets, where YOLOv4 had the highest performance at 40,000 iterations, 72% mAP, and 63% recall. While YOLOv3 has achieved maximum at 36000 iterations, 65.53% mAP, and 54% recall, Faster R-CNN has achieved maximum at 36,000 iterations, 51% mAP, and 49% recall. According to the results, YOLOv4 performed the best compared to YOLOv3 and Faster R-CNN.