YOLO Network-based URL Detection in Varied Conditions with Small-Sample Insights

Abstract

Object detection is a key technique in computer vision, as it is considered a necessary step in any recognition process. It is the procedure for determining the instance of the class to which the object belongs and estimating its location by displaying its bounding box. It was widely accepted that advances in object detection have generally gone through two periods: ”the traditional object detection period”, where detection was performed through classical machine learning techniques, and ”the deep learning based detection period”, where classical machine learning techniques have been completely replaced by methods based on deep neural networks. In this paper, we will focus on object detection based on deep learning. The main objective is to carry out a comparative study of three models of the YOLO family, already proven to be effective for object detection that are YOLOv3, YOLOv4, and YOLOv5 in the context of the detection of URLs in photos taken by a mobile phone. The experimental results, expressed in terms of average precision, showed the generalization ability of the three models, YOLOv3, YOLOv4, and YOLOv5. In addition, the stability of the YOLOV4 model against several difficulties added to the images.