摘要
利用施工现场监控视频或图像对工人进行精确检测,可为施工安全智能化管理提供基础支持。然而,较远的监控距离使得工人在画面中以小目标的形式出现,加之现场环境复杂多变,给工人检测带来了挑战。为此,本文提出了一种融合改进的YOLO模型与帧差法的小目标工人检测方法。一是通过改进YOLOv5模型对静态工人进行检测,即引入切片辅助推理(SAHI)获取小目标工人更清晰的特征,添加小目标检测头确保小目标对象特征的完整性,利用高效通道注意力机制(ECA)提高对小目标的检测效果;二是通过帧差法对图像特征较弱的运动工人进行检测,一定程度上弥补图像检测的不足。该方法在自建数据集上进行了验证,结果表明:改进后的YOLOv5模型F1-Score提升了11.3%,平均精确均值(mAP)提升了12.5%,而融合帧差法后的综合方法对小目标工人的检出率提高了3.6%,达到了84.2%,FPS达到6帧每秒,能更好地满足施工现场工人检测的需要。
The accurate detection of workers using construction site surveillance videos or images can support the intelligent management of construction safety.However,the long-distance surveillance causes workers to appear as small targets in images,and the complex and changing environment further complicates worker detection.To address this problem,a small-target worker detection method integrating an improved YOLO model with the frame difference method was proposed.On the one hand,the stationary workers were detected through the improved YOLOv5 model,which introduced slicing aided inference to obtain the clearer features of small-target workers,added small-target detection heads to ensure feature completeness,and employed the ECA mechanism to improve the detection performance.On the other hand,the moving workers with weak image features were detected using the frame difference method,to some extent compensating for the shortcomings of image detection.The proposed method was validated on a self-constructed dataset,and the results showed that the F1-Score and mAP50 of the improved YOLOv5 model improved by 11.3%and 12.5%,respectively.Additionally,the detection rate of small-target workers using the integrated method increased by 3.6%to 84.2%,with a FPS of 6 frames·per second.Thus,the proposed method can better satisfy the needs of worker detection on construction sites.
作者
李建华
韩宇
石开铭
张可嘉
郭红领
方东平
曹佳明
LI Jianhua;HAN Yu;SHI Kaiming;ZHANG Kejia;GUO Hongling;FANG Dongping;CAO Jiaming(Department of Construction Management,Tsinghua University,Beijing 100084,China;Beijing Urban Construction Group Co.Ltd,Beijing 100088,China)
出处
《图学学报》
CSCD
北大核心
2024年第5期1040-1049,共10页
Journal of Graphics
基金
国家自然科学基金面上项目(52278310)。
关键词
现场工人检测
小目标检测
YOLO
帧差法
site worker detection
small target detection
YOLO
frame difference method
