CPAM-P2-YOLOv8：基于YOLOv8改进的用于安全帽检测的算法
CPAM-P2-YOLOv8: Safety Helmet Detection Algorithm Based on the Improved YOLOv8
DOI: 10.12677/aam.2024.1310424, PDF,   
作者: 徐洪帆：青岛大学数学与统计学院，山东 青岛
关键词: 目标检测；YOLOv8；CPAM；小目标检测；知识蒸馏；Object Detection； YOLOv8； CPAM； Small Object Detection； Knowledge Distillation
摘要: 安全帽在保护施工人员免受事故伤害方面发挥着至关重要的作用。然而，由于种种原因，工人们并没有严格执行戴安全帽的规定。为了检测工人是否佩戴安全帽，本文提出了基于YOLOv8改进的目标检测算法(CPAM-P2-YOLOv8)。在YOLOv8的颈部添加CPAM结构，增强网络对图片的特征提取，在YOLOv8的头部引入处理后的小目标检测层P2。CPAM-P2-YOLOv8提高了目标检测的精确度，实验结果表明，改进模型的精确度达到了91%。与YOLOv8对比，CPAM-P2-YOLOv8的mAP50提高了1.0%，参数量减少了17%，同时通过对比发现，CPAM-P2-YOLOv8比YOLOv8在检测小目标方面更有优势。与YOLOv10对比，CPAM-P2-YOLOv8的mAP50提高1.9%。使用知识蒸馏方法，使CPAM-P2-YOLOv8的精确度进一步提升，达到91.4%。
Abstract: Safety helmets play a crucial role in protecting construction workers from accidents and injuries. However, due to various reasons, workers did not strictly adhere to the rule of wearing safety helmets. To detect whether workers are wearing helmets, this article proposes an improved object detection algorithm based on YOLOv8 (CPAM-P2-YOLOv8). Add CPAM structure to the neck network of YOLOv8 to enhance the feature extraction of safety helmets, and introduce a processed small object detection layer P2 at the head of YOLOv8. CPAM-P2-YOLOv8 improved the accuracy of object detection, and experimental results showed that the improved model achieved an accuracy of 91%. Compared with the YOLOv8 model, CPAM-P2-YOLOv8 improved mAP50 by 1.0% and reduced parameter count by 17%. Through comparison, it was found that CPAM-P2-YOLOv8 has more advantages in detecting small targets than YOLOv8. Compared with YOLOv10, the mAP50 of CPAM-P2-YOLOv8 increased by 1.9%. By using the knowledge distillation, the precision of CPAM-P2-YOLOv8 was further improved to 91.4%.
文章引用：徐洪帆. CPAM-P2-YOLOv8：基于YOLOv8改进的用于安全帽检测的算法[J]. 应用数学进展, 2024, 13(10): 4449-4458. https://doi.org/10.12677/aam.2024.1310424

参考文献

[1] Xu, Y., Yu, G., Wang, Y., Wu, X. and Ma, Y. (2016) A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images. Sensors, 16, Article No. 1325. [Google Scholar] [CrossRef] [PubMed]
[2] Girshick, R., Donahue, J., Darrell, T. and Malik, J. (2014) Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, 23-28 June 2014, 580-587. [Google Scholar] [CrossRef] 
[3] Girshick, R. (2015) Fast R-CNN. 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 7-13 December 2015, 1440-1448. [Google Scholar] [CrossRef] 
[4] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., et al. (2016) SSD: Single Shot MultiBox Detector. Computer Vision—ECCV 2016, Amsterdam, 11-14 October 2016, 21-37. [Google Scholar] [CrossRef] 
[5] Redmon, J., Divvala, S., Girshick, R. and Farhadi, A. (2016) You Only Look Once: Unified, Real-Time Object Detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 27-30 June 2016, 779-788. [Google Scholar] [CrossRef] 
[6] Wu, J., Cai, N., Chen, W., Wang, H. and Wang, G. (2019) Automatic Detection of Hardhats Worn by Construction Personnel: A Deep Learning Approach and Benchmark Dataset. Automation in Construction, 106, Article 102894. [Google Scholar] [CrossRef] 
[7] Shrestha, K., Shrestha, P.P., Bajracharya, D. and Yfantis, E.A. (2015) Hard-Hat Detection for Construction Safety Visualization. Journal of Construction Engineering, 2015, Article 721380. [Google Scholar] [CrossRef] 
[8] Liu, Y., Sun, P., Wergeles, N. and Shang, Y. (2021) A Survey and Performance Evaluation of Deep Learning Methods for Small Object Detection. Expert Systems with Applications, 172, Article 114602. [Google Scholar] [CrossRef] 
[9] Kang, M., Ting, C., Ting, F.F. and Phan, R.C.-W. (2024) ASF-YOLO: A Novel YOLO Model with Attentional Scale Sequence Fusion for Cell Instance Segmentation. Image and Vision Computing, 147, Article 105057. [Google Scholar] [CrossRef] 
[10] Lin, T., Dollar, P., Girshick, R., He, K., Hariharan, B. and Belongie, S. (2017) Feature Pyramid Networks for Object Detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, 21-26 July 2017, 936-944. [Google Scholar] [CrossRef] 
[11] Zhou, F., Zhao, H. and Nie, Z. (2021) Safety Helmet Detection Based on YOLOv5. 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA), Shenyang, 22-24 January 2021, 6-11. [Google Scholar] [CrossRef] 
[12] Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J. and Ding, G. (2024) YOLOv10: Real-Time End-to-End Object Detection. arXiv: 2405.14458. [Google Scholar] [CrossRef] 
[13] Shu, C., Liu, Y., Gao, J., Yan, Z. and Shen, C. (2021) Channel-Wise Knowledge Distillation for Dense Prediction. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, 10-17 October 2021, 5291-5300. [Google Scholar] [CrossRef] 

为你推荐