Abstract
Object detection is a pivotal aspect of computer vision, essential for diverse recognition tasks. This study centers on exploring deep learning methodologies for object detection, specifically targeting the identification of URLs in images captured by mobile phones. We conduct a comparative analysis of three models from the YOLO family – YOLOv3, YOLOv4, and YOLOv5 – recognized for their efficacy in object detection. Our research addresses the unique challenge of detecting URLs in images, particularly considering the limited availability of URL-labeled dataset. Through rigorous experimentation and evaluation, we demonstrate the generalization capabilities of YOLOv3, YOLOv4, and YOLOv5, as measured by average precision scores. Furthermore, we highlight the resilience of the YOLOv4 model against various image-related challenges. Our findings contribute significantly to the advancement of computer vision, specifically in the domain of object detection for real-world applications. By evaluating the performance of cutting-edge deep learning models, we provide valuable insights into their effectiveness for URL detection, thereby enriching our understanding of their practical utility. This research serves as a foundation for future investigations aimed at leveraging deep learning techniques to enhance object detection accuracy across diverse contexts.
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