Zhuangzhuang Nie
ABSTRACT
Aiming to address the issues of insufficient model training, low detection accuracy, and poor generalization capabilities caused by the low quality of the current goggles-wearing detection dataset, insufficient samples, and imbalanced sources, a goggles-wearing detection dataset that includes a variety of real and complex scenarios was constructed. An improved object detection algorithm based on Faster R-CNN was proposed to detect the wearing of protective goggles. This algorithm improves upon the Faster R-CNN by refining a more reasonable loss function, solving the problem of ineffective calculation of loss under special circumstances. At the same time, PAFPN is used to replace the original FPN, allowing the detection model to have a bottom-to-up secondary fusion, effectively enhancing feature extraction capabilities. Experimental results on the goggles-wearing detection dataset indicate that the improved Faster R-CNN model has an average precision of 82.7%. Compared to the Faster R-CNN model, the average precision has increased by 3.8%, enabling the detection of goggles wearing in complex environments.
- Huang Qin, Wang Jingwei, Huang Wangxing, Zhang Yuting, Zhou Lina. Clinical analysis of 1851 hospitalized patients with eye trauma [J]. Guangdong Med,2019,40(S1):251-253. (in Chinese)Google Scholar
- Joseph R, Santosh K D, Ross B G, ali F, You Only Look Once: Unified, Real-Time Object Detection[C]// Computer Vision and Pattern Recognition, 2016, abs/1506.02640(1): 779-788.Google Scholar
- Redmon J, Farhadi A. YOLO9000: Better, Faster, Stronger[C]// IEEE Conference on Computer Vision & Pattern Recognition. IEEE, 2017:6517-6525.Google Scholar
- Redmon J, Farhadi A. Yolov3: An incremental improvement[J]. arXiv preprint arXiv:1804.02767, 2018.Google Scholar
- Bochkovskiy A, Wang C Y, Liao H Y M. Yolov4: Optimal speed and accuracy of object detection[J]. arXiv preprint arXiv:2004.10934, 2020.Google Scholar
- Ultralytics. YOLOv5 [CP/OL]. [2020-08-09]. https://github. com/ultralytics/yolov5.Google Scholar
- Ross B. Girshick. Fast R-CNN.[C]// International Conference on Computer Vision, 2015, abs/1504.08083(): 1440-1448.Google Scholar
- Ren, S, He, K, Ross B G, Sun, J, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[C]// Neural Information Processing Systems, 2017, 39(6): 1137-1149.Google Scholar
- Ren, S, He, K, Ross B G, Sun, J, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[C]// Neural Information Processing Systems, 2017, 39(6): 1137-1149.Google Scholar
- ZHAO Yongqiang, Rao Yuan, Dong Shipeng Review of Deep learning object detection methods [J]. Journal of Image and Graphics, 2019,25(04):629-654. (in Chinese)Google Scholar
- Adedeji Olugboja, Zenghui Wang, and Yanxia Sun, "Parallel Convolutional Neural Networks for Object Detection," Journal of Advances in Information Technology, Vol. 12, No. 4, pp. 279-286, November 2021. doi: 10.12720/jait.12.4.279-286Google ScholarCross Ref
- Srikanth Bethu, M. Neelakantappa, A. Swami Goud, B. Hari Krishna, and P. N. V. Syamala Rao M, "An Approach for Person Detection along with Object Using Machine Learning," Journal of Advances in Information Technology, Vol. 14, No. 3, pp. 411-417, 2023.Google ScholarCross Ref
- Zhang Dengkui. Design and implementation of Goggles Wearing detection System based on Deep learning [D]. Beijing University of Posts and Telecommunications,2021.Google Scholar
- Liu Fan, Wang Ying, YAN Guoyu Image perception hashing algorithm based on Difference [J]. Computer Engineering and Design,2021,42(03):782-789. (in Chinese)Google Scholar
- Luyl-Da Quach, Khang Nguyen Quoc, Anh Nguyen Quynh, and Hoang Tran Ngoc, "Evaluating the Effectiveness of YOLO Models in Different Sized Object Detection and Feature-Based Classification of Small Objects," Journal of Advances in Information Technology, Vol. 14, No. 5, pp. 907-917, 2023.Google ScholarCross Ref
- Zheng Z, Wang P, Liu W, Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression[J]. Proceedings of the AAAI Conference on Artificial Intelligence,2020,34(07).Google Scholar
- Zhaohui Z, Ping W, Dongwei R, Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation. [J]. IEEE transactions on cybernetics, 2021, PP.Google Scholar
- Lin T,Dollár P, Girshick B R, Feature Pyramid Networks for Object Detection.[J].CoRR,2016,abs/1612. 03144.Google Scholar
- Liu S,Qi L,Qin H, Path Aggregation Network for Instance Segmentation.[J]. CoRR,2018.Google Scholar
Index Terms
- Goggle Wear Detection Algorithm based on Improved Faster R-CNN
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