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:2019,28(9):174-179
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基于YOLO的安全帽检测方法
(1.太原科技大学 计算机科学与技术学院, 太原 030024;2.中国科学院 地理科学与资源研究所, 北京 100101)
Safety Helmet Detection Based on YOLO
(1.School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China;2.Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)
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投稿时间:2019-03-01    修订日期:2019-03-29
中文摘要: 安全帽作为作业工人最基本的个体防护装备,对作业人员的生命安全具有重要意义.但是部分作业人员安全意识缺乏,不佩戴安全帽行为时常发生.本文聚焦于复杂场景下对工作人员是否佩戴安全帽的实时检测.YOLO (You Only Look Once)是当前最为先进的实时目标检测算法,在检测精度和速度上都取得了良好的效果,将YOLO应用于安全帽检测.针对未佩戴安全帽单类检测问题,修改分类器,将输出修改为18维度的张量.基于YOLOv3在ImageNet上的预训练模型,对实际场景下采集到的2010张样本进行标注并训练,根据损失函数和IOU曲线对模型进行优化调参,最终得到最优的安全帽检测模型.实验结果表明,在2000张图片测试集上取得了98.7%的准确率,在无GPU环境下平均检测速度达到了35 fps,满足实时性的检测要求,验证了基于YOLOv3安全帽检测方法的有效性.
Abstract:As the most basic personal protective equipment, helmets are of great significance to the safety for workers. However, some workers lack safety awareness and often do not wear safety helmets. This study focuses on the detection of safety helmet in complex background. You Only Look Once (YOLO) is a state-of-the-art, real-time object detection algorithm, we propose to apply the YOLO detector for safety helmets detection, which achieves high accuracy. For the single-type detection problem without wearing a helmet, the classifier is modified and the output is modified to a tensor of 18 dimensions. We train YOLOv3 for safety helmets detection on the 2010 datasets based on the pre-training model in ImageNet. Then we optimize the model according to the loss function and IOU curve. The experimental results show that the safety helmet detector gets 98.7% accuracy and 35 fps on the 2000 detection test sets without GPU, which meets the real-time detection requirements. The effectiveness of the YOLOv3 safety helmet detection method is verified.
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基金项目:山西省中科院科技合作项目(20141101001);山西省重点研发计划(一般)工业项目(201703D121042-1);山西省社会发展科技项目(20140313020-1);山西省应用基础研究项目(201801D221179)
引用文本:
林俊,党伟超,潘理虎,白尚旺,张睿.基于YOLO的安全帽检测方法.计算机系统应用,2019,28(9):174-179
LIN Jun,DANG Wei-Chao,PAN Li-Hu,BAI Shang-Wang,ZHANG Rui.Safety Helmet Detection Based on YOLO.COMPUTER SYSTEMS APPLICATIONS,2019,28(9):174-179

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