单阶段安全帽检测深度学习算法综述

doi:10.3778/j.issn.1002-8331.2312-0034

摘要/Abstract

摘要： 安全帽佩戴实时检测是智慧工地和智慧交通必不可少的一部分，基于深度学习的安全帽检测逐渐取代了传统的检测方法，在精度、性能和效率等方面取得了显著进展，在现实场景中有了广泛的应用。为了便于安全帽算法的研究，综合分析了各应用场景中安全帽目标检测算法的研究现状。总结了目标检测算法的发展历史；对近年来国内外学者的安全帽检测算法研究进行归纳，对比总结了不同算法不同优化的优缺点，着重分析了安全帽检测算法的轻量化方法；根据目前目标检测算法在实际应用场景中出现的不足，对安全帽检测的深度学习算法的未来研究方向进行了展望。

关键词: 深度学习, 目标检测算法, 安全帽检测

Abstract: Real-time detection of safety helmet-wearing is an essential part of smart construction sites and smart traffic, safety helmet detection based on deep learning has gradually replaced the traditional detection methods, and has made significant progress in accuracy, performance, and efficiency. It has been widely used in real scenarios. To facilitate the future research of safety helmet-wearing algorithms, the research status of object detection algorithms for safety helmets in various application scenarios is analyzed comprehensively. Firstly, the history of the object detection algorithm is summarized. Secondly, the advantages and disadvantages of different algorithms and optimizations are analyzed, and the lightweight safety helmet detection algorithms are discussed. Finally, according to the shortages of the current object detection algorithm applied in the actual scene, the future direction of a deep learning algorithm for safety helmet detection is prospected.

Key words: deep learning, object detection algorithm, safety helmet detection

管含宇, 凌云, 汪舒磊. 单阶段安全帽检测深度学习算法综述[J]. 计算机工程与应用, 2024, 60(16): 61-75.

GUAN Hanyu, LING Yun, WANG Shulei. Review of Deep Learning Algorithms for One-Stage Safety Helmet Detection[J]. Computer Engineering and Applications, 2024, 60(16): 61-75.

参考文献

[1] 董文轩, 梁宏涛, 刘国柱, 等. 深度卷积应用于目标检测算法综述[J]. 计算机科学与探索, 2022, 16(5): 1025-1042.
DONG W X, LIANG H T, LIU G Z, et al. Review of deep convolution applied to target detection algorithms[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(5): 1025-1042.
[2] 张立艺, 武文红, 牛恒茂, 等. 深度学习中的安全帽检测算法应用研究综述[J]. 计算机工程与应用, 2022, 58(16): 1-17.
ZHANG L Y, WU W H, NIU H M, et al. Summary of application research on helmet detection algorithm based on deep learning[J]. Computer Engineering and Applications, 2022, 58(16): 1-17.
[3] 李政谦, 刘晖. 基于深度学习的安全帽佩戴检测算法综述[J]. 计算机应用与软件, 2022, 39(6): 194-202.
LI Z Q, LIU H. Helmet wearing detection algorithm based on deep learning[J]. Computer Applications and Software, 2022, 39(6): 194-202.
[4] 高腾, 张先武, 李柏. 深度学习在安全帽佩戴检测中的应用研究综述[J]. 计算机工程与应用, 2023, 59(6): 13-29.
GAO T, ZHANG X W, LI B. Review on application of deep learning in helmet wearing detection[J]. Computer Engineering and Applications, 2023, 59(6): 13-29.
[5] SHETTY S. Application of convolutional neural network for image classification on Pascal VOC challenge 2012 dataset[J]. arXiv:1607.03785, 2016.
[6] LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: common objects in context[C]//Proceedings of the 13th European Conference on Computer Vision, Zurich, Sep 6-12, 2014. Cham: Springer, 2014: 740-755.
[7] DENG J, DONG W, SOCHER R, et al. ImageNet: a large-scale hierarchical image database[C]//Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009: 248-255.
[8] KUZNETSOVA A, ROM H, ALLDRIN N, et al. The open images dataset v4: unified image classification, object detection, and visual relationship detection at scale[J]. International Journal of Computer Vision, 2020, 128(7): 1956-1981.
[9] GEIGER A, LENZ P, STILLER C, et al. Vision meets robotics: the KITTI dataset[J]. The International Journal of Robotics Research, 2013, 32(11): 1231-1237.
[10] OJALA T, PIETIKAINEN M, HARWOOD D. Performance evaluation of texture measures with classification based on kullback discrimination of distributions[C]//Proceedings of 12th International Conference on Pattern Recognition, 1994, 1: 582-585.
[11] PAPAGEORGIOU C P, OREN M, POGGIO T. A general framework for object detection[C]//Proceedings of the 6th International Conference on Computer Vision, 1998: 555-562.
[12] LINDEBERG T. Scale invariant feature transform[J]. Scholarpedia, 2012, 7(5): 10491.
[13] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]//Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005: 886-893.
[14] ZITNICK C L, DOLLáR P. Edge boxes: locating object proposals from edges[C]//Proceedings of the 13th European Conference on Computer Vision, Zurich, Sep 6-12, 2014. Cham: Springer, 2014: 391-405.
[15] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580-587.
[16] GIRSHICK R. Fast R-CNN[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision, 2015: 1440-1448.
[17] QIAN R, LIU Q, YUE Y, et al. Road surface traffic sign detection with hybrid region proposal and fast R-CNN[C]//Proceedings of the 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, 2016: 555-559.
[18] HE K, ZHANG X, REN S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916.
[19] REN S, HE K, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks [C]//Advances in Neural Information Processing Systems 28, 2015.
[20] HMIDANI O, ALAOUI E M I. A comprehensive survey of the R-CNN family for object detection[C]//Proceedings of the 2022 5th International Conference on Advanced Communication Technologies and Networking, 2022: 1-6.
[21] CHEN Z, WANG L, LI B, et al. An improved Faster R-CNN transmission line bolt defect detection method[C]//Proceedings of the 2022 World Automation Congress, 2022: 82-85.
[22] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779-788.
[23] REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 7263-7271.
[24] IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]//Proceedings of the 2015 International Conference on Machine Learning, 2015: 448-456.
[25] REDMON J, FARHADI A. YOLOV3: an incremental improvement[J]. arXiv:1804.02767, 2018.
[26] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
[27] CHENG R, HE X, ZHENG Z, et al. Multi-scale safety helmet detection based on SAS-YOLOv3-tiny[J]. Applied Sciences, 2021, 11(8): 3652.
[28] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[J]. arXiv:2004.10934, 2020.
[29] WANG C Y, LIAO H Y M, WU Y H, et al. CSPNet: a new backbone that can enhance learning capability of CNN[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020: 390-391.
[30] LIU S, QI L, QIN H, et al. Path aggregation network for instance segmentation[C]//Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018: 8759-8768.
[31] LI C, LI L, JIANG H, et al. YOLOv6: a single-stage object detection framework for industrial applications[J]. arXiv: 2209.02976, 2022.
[32] DING X, ZHANG X, MA N, et al. RepVGG: making VGG-style convnets great again[C]//Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 13733-13742.
[33] WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 7464-7475.
[34] ZHANG X, ZENG H, GUO S, et al. Efficient long-range attention network for image super-resolution[C]//Proceedings of the 17th European Conference on Computer Vision. Cham: Springer, 2022: 649-667.
[35] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//Proceedings of the 14th European Conference on Computer Vision, Amsterdam, Oct 11-14, 2016. Cham: Springer, 2016: 21-37.
[36] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision, 2017: 2980-2988.
[37] TAN M, PANG R, LE Q V. EfficientDet: scalable and efficient object detection[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 10781-10790.
[38] TIAN Z, SHEN C, CHEN H, et al. FCOS: fully convolutional one?stage object detection[C]//Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019: 9627-9636.
[39] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. arXiv:1409. 1556, 2014.
[40] HOWARD A G, ZHU M, CHEN B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications[J]. arXiv:1704.04861, 2017.
[41] ZHENG X, YAO J, XU X. Violation monitoring system for power construction site[C]//Proceedings of the IOP Conference Series: Earth and Environmental Science, 2019.
[42] HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4700-4708.
[43] WU F, JIN G, GAO M, et al. Helmet detection based on improved YOLO V3 deep model[C]//Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, 2019: 363-368.
[44] 曹燕. 基于深度学习的建筑工人安全帽检测方法研究[D]. 苏州: 苏州科技大学, 2021.
CAO Y. Research on detection methods of construction workers’ safety helmets based on deep learning[D]. Suzhou: Suzhou University of Science and Technology, 2021.
[45] HAN J, LIU Y, LI Z, et al. Safety helmet detection based on YOLOv5 driven by super-resolution reconstruction[J]. Sensors, 2023, 23(4): 1822.
[46] GENG R, MA Y, HUANG W. An improved helmet detection method for YOLOv3 on an unbalanced dataset[C]//Proceedings of the 2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication, 2021: 328-332.
[47] CHANG H, XU Y, HUANG S, et al. Multiple complex weather tolerant and low cost solution for helmet detection[J]. IEEE Access, 2023, 11: 50264-50271.
[48] 邓庆昌. 工地施工现场的安全帽检测算法研究与系统实现[D]. 镇江: 江苏科技大学, 2022.
DENG Q C. Algorithm research and system realization of safety helmet detection on construction site[D]. Zhenjiang: Jiangsu University of Science and Technology, 2022.
[49] ZHANG Y, QIU Y, BAI H. FEFD-YOLOV5: a helmet detection algorithm combined with feature enhancement and feature denoising[J]. Electronics, 2023, 12(13): 2902.
[50] DONG G, ZHANG Y, XIE W, et al. A safety helmet-wearing detection method based on cross-layer connection[J]. Journal of Real-Time Image Processing, 2024, 21(3): 1-10.
[51] HAN K, ZENG X. Deep learning-based workers safety helmet wearing detection on construction sites using multi-scale features[J]. IEEE Access, 2022, 10: 718-729.
[52] DHYANJITH G, MANOHAR N, RAJ A G V. Helmet detection using YOLO V3 and single shot detector[C]//Proceedings of the 2021 6th International Conference on Communication and Electronics Systems, 2021: 1844-1848.
[53] HAO S, HE J, LUO L, et al. Helmet wearing detection algorithm based on improved YOLOX[C]//Proceedings of the 2022 5th International Conference on Pattern Recognition and Artificial Intelligence, 2022: 316-321.
[54] LIU J, LIU L. Helmet wearing detection based on YOLOv4-MT[C]//Proceedings of the 2021 4th International Conference on Robotics, Control and Automation Engineering, 2021: 1-5.
[55] FANG J, LIN X, ZHOU F, et al. Safety helmet detection based on optimized YOLOv5[C]//Proceedings of the 2023 Prognostics and Health Management Conference, 2023: 117-121.
[56] DENG B, LEI X, YE M. Safety helmet detection method based on YOLO v4[C]//Proceedings of the 2020 16th International Conference on Computational Intelligence and Security, 2020: 155-158.
[57] CHEN J, ZHU J, LI Z, et al. YOLOv7-WFD: a novel convolutional neural network model for helmet detection in high-risk workplaces[J]. IEEE Access, 2023, 11: 113580-113592.
[58] WANG L, ZHANG X, YANG H. Safety helmet wearing detection model based on improved YOLO-M[J]. IEEE Access, 2023, 11: 26247-26257.
[59] ARANI E, GOWDA S, MUKHERJEE R, et al. A comprehensive study of real-time object detection networks across multiple domains: a survey[J]. arXiv:2208.10895, 2022.
[60] 陈家亮. 基于改进YOLOv5的安全帽佩戴检测算法[D]. 长春: 吉林大学, 2023.
CHEN J L. A helmet wearing detection algorithm based on the improved YOLOv5[D]. Changchun: Jilin University, 2023.
[61] TAN S, LU G, JIANG Z, et al. Improved YOLOv5 network model and application in safety helmet detection[C]//Proceedings of the 2021 IEEE International Conference on Intelligence and Safety for Robotics, 2021.
[62] AGORKU G, AGBOBLI D, CHOWDHURY V, et al. Real-time helmet violation detection using YOLOv5 and ensemble learning [J]. arXiv:2304.09246, 2023.
[63] TAI W, WANG Z, LI W, et al. DAAM-YOLOV5: a helmet detection algorithm combined with dynamic anchor box and attention mechanism[J]. Electronics, 2023, 12(9): 2094.
[64] 熊江宜. 基于深度学习的轻量级安全帽佩戴检测算法研究[D]. 荆州: 长江大学, 2021.
XIONG J Y. A research on lightweight safety helmet detection algorithm based on deep learning[D]. Jingzhou: Yangtze University, 2021.
[65] 刘超. 基于YOLOv4-tiny的安全帽佩戴检测算法研究[D]. 南京: 南京林业大学, 2022.
LIU C. Research on helmet wearing detection algorithm based on YOLOv4-tiny[D]. Nanjing: Nanjing Forestry University, 2022.
[66] FU L. Construction site safety helmet wearing detection method based on improved YOLOv5[J]. Journal of Physics: Conference Series, 2023, 2560(1): 012042.
[67] ZHAN H, PEI X. Based on improved single shot multibox detector construction site helmet detection algorithm[C]//Proceedings of the 2023 5th International Conference on Communications, Information System and Computer Engineering, 2023: 425-428.
[68] DATH M K, RAKHRA M, SINGH D, et al. Basic design for the implementation of automatic surveillance system on helmet detection[C]//Proceedings of the 2022 4th International Conference on Artificial Intelligence and Speech Technology, 2022: 1-5.
[69] MOHAN P, NARAYAN P, SHARMA L, et al. Helmet detection using faster region-based convolutional neural networks and single-shot multibox detector[C]//Proceedings of the 2021 8th International Conference on Smart Computing and Communications, 2021: 209-214.
[70] VIGNESH R A G, MANOHAR N, DHYANJITH G. Helmet detection using single shot detector (SSD)[C]//Proceedings of the 2021 2nd International Conference on Electronics and Sustainable Communication Systems, 2021: 1241-1244.
[71] FENG H, HU J. Helmet wearing detection using improved single shot multibox detector[C]//Proceedings of the 2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing, 2022: 444-447.
[72] WANG Y. Research on a safety helmet detection method based on smart construction site[C]//Proceedings of the 2021 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, 2021: 341-343.
[73] CHEN X, XIE Q. Safety helmet-wearing detection system for manufacturing workshop based on improved YOLO-v7[J]. Journal of Sensors, 2023. DOI:10.1155/2023/7230463.
[74] SOLTANIKAZEMI E, DHAKAL A, HATUWAL B K, et al. Real-time helmet violation detection in AI city challenge 2023 with genetic algorithm-enhanced YOLOv5[C]//Proceedings of the 2023 IEEE Applied Imagery Pattern Recognition Workshop, 2023.
[75] LI X, WANG W, WU L, et al. Generalized focal loss: learning qualified and distributed bounding boxes for dense object detection[C]//Advances in Neural Information Processing Systems 33, 2020: 21002-21012.
[76] 吕宗喆, 徐慧, 杨骁, 等. 面向小目标的YOLOv5安全帽检测算法[J]. 计算机应用, 2023, 43(6): 1943-1949.
LV Z Z, XU H, YANG X, et al. Small object detection algorithm of YOLOv5 for safety helmet[J]. Journal of Computer Applications, 2023, 43(6): 1943-1949.
[77] LIAO W. Research on detection algorithm of safety helmet based on improved YOLOX[J]. Frontiers in Computing and Intelligent Systems, 2023, 3(2): 136-138.
[78] HAN K, WANG Y, TIAN Q, et al. GhostNet: more features from cheap operations[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 1580-1589.
[79] ZAIDI S S A, ANSARI M S, ASLAM A, et al. A survey of modern deep learning based object detection models[J]. Digital Signal Processing, 2022, 126: 103514.
[80] ALFONSO R, DAHER C, ARZAMENDIA M, et al. A motorcyclist helmet detection system through a two-stage CNN approach[C]//Proceedings of the 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, 2021: 1-6.
[81] DENG L, LI H, LIU H, et al. A lightweight YOLOv3 algorithm used for safety helmet detection[J]. Scientific Reports, 2022, 12(1): 10981.
[82] 赵万福. 基于深度学习的安全帽佩戴检测算法研究[D]. 西安: 长安大学, 2022.
ZHAO W F. Research on detection algorithm of helmet wearing based on deep learning[D]. Xi’an: Chang’an University, 2022.
[83] CHEN J, DENG S, WANG P, et al. Lightweight helmet detection algorithm using an improved YOLOv4[J]. Sensors, 2023, 23(3): 1256.
[84] REN D, SUN T, YU C, et al. Research on safety helmet detection for construction site[C]//Proceedings of the 2021 International Conference on Computer Information Science and Artificial Intelligence, 2021: 186-189.
[85] 郭奕裕, 周箩鱼. 安全帽佩戴检测网络模型的轻量化设计[J]. 计算机工程, 2023, 49(4): 312-320.
GUO Y Y, ZHOU L Y. Lightweight design of safety helmet wearing detection network model[J]. Computer Engineering, 2023, 49(4): 312-320.
[86] DENG Z, YAO C, YIN Q. Safety helmet wearing detection based on jetson nano and improved YOLOv5[J]. Advances in Civil Engineering, 2023. DOI:10.1155/2023/1959962.
[87] 李延满, 王必恒, 赵羚焱. 基于轻量化YOLOv5的安全帽检测[J]. 计算机与现代化, 2023(10): 59-64.
LI Y M, WANG B H, ZHAO L Y. Safety helmet detection based on lightweight YOLOv5[J]. Computer and Modernization, 2023(10): 59-64.
[88] 赵红成, 田秀霞, 杨泽森, 等. YOLO-S: 一种新型轻量的安全帽佩戴检测模型[J]. 华东师范大学学报（自然科学版）, 2021(5): 134-145.
ZHAO H C, TIAN X X, YANG Z S, et al. YOLO-S：a new lightweight helmet wearing detection model[J]. Journal of East China Normal University (Natural Science), 2021(5): 134-145.
[89] 张传深, 徐升, 胡佳, 等. 基于巡逻无人机的轻量型安全帽佩戴检测方法与应用[J]. 集成技术, 2023, 12(4): 18-31.
ZHANG C S, XU S, HU J, et al. Research on safety helmet recognition method and application using patrol unmanned aerial vehicle[J]. Journal of Integration Technology, 2023, 12(4): 18-31.
[90] 梁思成. 基于卷积神经网络的安全帽检测研究[D]. 哈尔滨: 哈尔滨工业大学, 2021.
LIANG S C. Research on safety helmet wearing detection based on convolutional neural network[D]. Harbin: Harbin Institute of Technology, 2021.
[91] AN Q, XU Y, YU J, et al. Research on safety helmet detection algorithm based on improved YOLOv5s[J]. Sensors, 2023, 23(13): 5824.
[92] JIA C. Application effect analysis for helmet detection algorithm based on YOLOV5[J]. Highlights in Science, Engineering and Technology, 2023, 39: 1214-1220.
[93] TAO Y J, PAN L. Improved lightweight helmet wearing detection method for YOLOXs[C]//Proceedings of the 2022 China Automation Congress, 2022: 737-741.
[94] 徐帅驰. 基于深度学习的边端安全帽识别系统设计[D]. 荆州: 长江大学, 2023.
XU S C. Design of edge-end helmet recognition system based on deep learning[D]. Jingzhou: Yangtze University, 2023.
[95] 张翼翔, 林松, 李雪. 基于CenterNet-GhostNet的选煤厂危险区域人员检测[J]. 工矿自动化, 2022, 48(4): 66-71.
ZHANG Y X, LIN S, LI X. Personnel detection in dangerous area of coal preparation plant based on CenterNet-GhostNet[J]. Industry and Mine Automation, 2022, 48(4): 66-71.
[96] 黄品超, 刘石坚, 徐戈, 等. 关联增强改进的CenterNet安全帽检测方法[J]. 计算机工程与应用, 2023, 59(17): 250-256.
HUANG P C, LIU S J, XU G, et al. Helmet wearing detection method based on improved CenterNet with enhanced associations[J]. Computer Engineering and Applications, 2023, 59(17): 250-256.
[97] SHEN X, LI B, WANG X, et al. Yolov5-based channel pruning is used for real-time detection of construction workers’ safety helmets and anti-slip shoes in informationalized construction sites[J]. Journal of Physics: Conference Series, 2021. DOI:10.1088/1742-6596/2031/1/012027.