Foggy-Based Object Detection In Video Using Faster R-CNN, YOLOv3, and SSD
Lailia Rahmawati, Supriadi Rustad, Aris Marjuni, Moch Arief Soeleman, Pulung Nurtantio Andono, Pujiono Pujiono · 2023
This paper discusses a comparison of several methods used for object detection in bad weather conditions, such as the presence of fog that interferes with the object's view. The methods compared in this paper include Faster R-CNN, Yolov3, and SSD (Single Shot Multi-Box Detector). This work contributes to evaluating the best method to overcome the object detection problem in bad weather, which is measured by mean average precision (mAP). The evaluation is executed using video objects that are converted into frames. Based on experimental results, the Faster R-CNN method is superior in terms of the mAP value where the Faster R-CNN is 87%, using SSD is 48%, and Yolov3 is 31%. Faster R-CNN provides better performance than the other two methods because the basic idea of FRCNN is the use of a Region Proposal Network (RPN) in a feature map which aims to propose many objects that can be identified in a particular image.