摘要
安全帽佩戴检测对建筑工地的安全管理至关重要,现有的检测模型存在检测速度慢、检出精度低及难以实地部署等现象,据此提出一种基于YOLOv5s的轻量化安全帽检测模型KD-YOLO,该模型引入知识蒸馏的思想,将改进的轻量级网络作为学生网络,通过教师-学生的知识传递,来提升学生网络检测准确性;利用轻量级注意力模块Shuffle Attention,以增强对图像有用信息的关注;采用基于动态非单调聚焦机制的边界框损失Wise-IoU损失函数来平衡不同质量的锚框,减少误检和漏检。实验结果表明,改进的轻量化安全帽检测模型通过引入知识蒸馏等优化策略后网络的平均精度均值提升3.5%,参数量减少18.1%,浮点运算数减少1.8%,满足建筑工地实时安全帽佩戴检测需求,同时模型内存占用少,易于实地部署。
The detection of safety helmet wearing is crucial for safety management on construction sites.Exist⁃ing detection models suffer from slow detection speed,low detection accuracy,and difficulties in on-site de⁃ployment.Accordings,a lightweight safety helmet detection model,KD-YOLO,based on YOLO5s is pro⁃posed.This model incorporates the concept of knowledge distillation,using an improved lightweight network as the student network,to enhance the student network′s detection accuracy through teacher-student knowl⁃edge transfer.It employs the lightweight attention module,Shuffle Attention,to enhance the focus on useful information of the images.Additionally,it employs the Wise-IoU bounding box loss function based on a dy⁃namic non-monotonic focusing mechanism to balance anchors of different qualities,reducing false positives and false negatives.Experimental results show that the improved lightweight safety helmet detection model,optimized through knowledge distillation and other strategies,achieves a 3.5%increase in mAP,a 18.1%re⁃duction in parameter count,and a 1.8%reduction in floating-point operations,respectively.This model meets the real-time safety helmet wearing detection requirements on construction sites,while consuming less memo⁃ry and being easy to deploy on the construction sites.
作者
孙光灵
黄磊
吴倩
SUN Guangling;HUANG Lei;WU Qian(School of Electronic and Information Engineering,Anhui Jianzhu University,230601,Hefei,Anhui,China;Intelligent Interconnected Systems Laboratory of Anhui Province,Hefei University of Technology,230009,Hefei,Anhui,China)
出处
《淮北师范大学学报（自然科学版）》
CAS
2024年第2期41-48,共8页
Journal of Huaibei Normal University：Natural Sciences
基金
安徽省高等学校科学研究重点项目(2023AH050164)
安徽省住房城乡建设科学技术计划项目(2023-YF058,2023-YF113)
合肥工业大学开放基金项目(PA2021AKSK0107)。
