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

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.

Keywords

• Object Detection
• Deep Learning
• Convolutional Neural Networks (CNN)
• YOLO
• URL Detection
• Small Sample

References

1. Zuguo Chen, Yanglong Liu, Chaoyang Chen, Ming Lu, and Xuzhuo Zhang. Malicious url detection based on improved multilayer recurrent convolutional neural network model. Security and Communication networks, 2021:1–13, 2021.
2. Jianming Hu, Xiyang Zhi, Tianjun Shi, Wei Zhang, Yang Cui, and Shenggang Zhao. Pag-yolo: A portable attention-guided yolo network for small ship detection. Remote Sensing, 13(16):3059, 2021.
3. Yankai Ma, Jun Yang, Zhendong Li, and Ziqiang Ma. Yolo-cigarette: An effective yolo network for outdoor smoking real-time object detection. In 2021 Ninth International Conference on Advanced Cloud and Big Data (CBD), pages 121–126. IEEE, 2022.
4. Zhao Feng, Wang Jianzong, and Xiao Jing. Yolo-based image target recognition method and apparatus, electronic device, and storage medium. https://patents. google.com/patent/WO2020164282A1/en.
5. Jing Wang, Peng Yang, Yuansheng Liu, Duo Shang, Xin Hui, Jinhong Song, and Xuehui Chen. Research on improved yolov5 for low-light environment object detection. Electronics, 12(14):3089, 2023.
6. Michael Shenoda. Lighting and rotation invariant real-time vehicle wheel detector based on yolov5. https://arxiv.org/pdf/2305.17785, 2023.
7. Baokai Liu, Fengjie He, Shiqiang Du, Jiacheng Li, and Wenjie Liu. An advanced yolov3 method for small object detection. Journal of Intelligent & Fuzzy Systems, (Preprint):1–13, 2022.
8. Object detection for blind people using yolov3. International Journal For Science Technology And Engineering, 11(5):7172–7181, 2023.

9. Renduchinthala Sai Praneeth, Kancharla Chetan Sai Akash, Bommisetty Keerthi Sree, P Ithaya Rani, and Abhishek Bhola. Scaling object detection to the edge with yolov4, tensorflow lite. In 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), pages 1547–1552. IEEE, 2023.
10. Alberto Garcia-Garcia, Sergio Orts-Escolano, Sergiu Oprea, Victor Villena Martinez, and Jose Garcia-Rodriguez. A review on deep learning techniques applied to semantic segmentation. https://arxiv.org/pdf/1704.06857, 2017.
11. Zhengxia Zou, Keyan Chen, Zhenwei Shi, Yuhong Guo, and Jieping Ye. Object detection in 20 years: A survey. Proceedings of the IEEE, 2023.
12. Paul Viola and Michael Jones. Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001, volume 1, pages I–I. Ieee, 2001.
13. Navneet Dalal and Bill Triggs. Histograms of oriented gradients for human detection. In 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), volume 1, pages 886–893. Ieee, 2005.
14. Pedro F Felzenszwalb, Ross B Girshick, David McAllester, and Deva Ramanan. Object detection with discriminatively trained part-based models. IEEE transactions on pattern analysis and machine intelligence, 32(9):1627–1645, 2009.
15. Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 779–788, 2016.
16. Joseph Redmon and Ali Farhadi. Yolov3: An incremental improvement. https: //arxiv.org/pdf/1804.02767, 2018.
17. Seokyong Shin, Hyunho Han, and Sang Hun Lee. Improved yolov3 with duplex fpn for object detection based on deep learning. The International Journal of Electrical Engineering & Education, page 0020720920983524, 2021.
18. Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. Yolov4: Optimal speed and accuracy of object detection. https://arxiv.org/pdf/2004.10934, 2020.
19. Chien-Yao Wang, Hong-Yuan Mark Liao, Yueh-Hua Wu, Ping-Yang Chen, JunWei Hsieh, and I-Hau Yeh. Cspnet: A new backbone that can enhance learning capability of cnn. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 390–391, 2020.
20. Shu Liu, Lu Qi, Haifang Qin, Jianping Shi, and Jiaya Jia. Path aggregation network for instance segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8759–8768, 2018.
21. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence, 37(9):1904–1916, 2015.
22. Glenn Jocher. Ultralytics yolov5. https://github.com/ultralytics/yolov5, 2020.
23. Yiming Fang, Xianxin Guo, Kun Chen, Zhu Zhou, and Qing Ye. Accurate and automated detection of surface knots on sawn timbers using yolo-v5 model. BioResources, 16(3):5390, 2021