Safety helmets can reduce head injuries from object impacts and lower the probability of safety accidents,as well as being of great significance to construction safety.However,for a variety of reasons,construction wor...Safety helmets can reduce head injuries from object impacts and lower the probability of safety accidents,as well as being of great significance to construction safety.However,for a variety of reasons,construction workers nowadays may not strictly enforce the rules of wearing safety helmets.In order to strengthen the safety of construction site,the traditional practice is to manage it through methods such as regular inspections by safety officers,but the cost is high and the effect is poor.With the popularization and application of construction site video monitoring,manual video monitoring has been realized for management,but the monitors need to be on duty at all times,and thus are prone to negligence.Therefore,this study establishes a lightweight model YOLO_CA based on YOLOv5 for the automatic detection of construction workers’helmet wearing,which overcomes the shortcomings of the current manual monitoring methods that are inefficient and expensive.The coordinate attention(CA)addition to the YOLOv5 backbone strengthens detection accuracy in complex scenes by extracting critical information and suppressing non-critical information.Further parameter compression with deeply separable convolution(DWConv).In addition,to improve the feature representation speed,we swap out C3 with a Ghost module,which decreases the floating-point operations needed for feature channel fusion,and CIOU_Loss was substituted with EIOU_Loss to enhance the algorithm’s localization accuracy.Therefore,the original model needs to be improved so as to enhance the detection of safety helmets.The experimental results show that the YOLO_CA model achieves good results in all indicators compared with the mainstream model.Compared with the original model,the mAP value of the optimized model increased by 1.13%,GFLOPs cut down by 17.5%,and there is a 6.84%decrease in the total model parameters,furthermore,the weight size cuts down by 4.26%,FPS increased by 39.58%,and the detection effect and model size of this model can meet the requirements of lightweight embedding.展开更多
针对YOLO v5l(you only look once version 5 large)算法对于小目标、少样本且背景复杂的排水管道缺陷图像检测的精度低、误检和漏检率较高等问题,提出了一种基于YOLO v5l-Im算法的排水管道缺陷检测改进方法。做了三点改进:首先提出了Fo...针对YOLO v5l(you only look once version 5 large)算法对于小目标、少样本且背景复杂的排水管道缺陷图像检测的精度低、误检和漏检率较高等问题,提出了一种基于YOLO v5l-Im算法的排水管道缺陷检测改进方法。做了三点改进:首先提出了Focal-EIoU(focal embedding intersection over union)损失函数,有效提升了检测模型的性能;其次为增强检测模型对小目标缺陷的检测效果,减少缺陷误检和漏检的概率,将骨干网络中浅层特征图融合到双向特征金字塔网络(bidirectional feature pyramid network,BiFPN)中,增加针对小目标的预测层;最后在YOLO v5l中引入坐标注意力机制(coordinate attention,CA),提高模型对图像中感兴趣区域的敏感程度,减少冗余背景信息的干扰。3种改进对平均检测准确率(mean average precision,mAP)的提升分别为2.0、2.9、5.9个百分点。将三种有效改进融合到一起,检测结果表明:本文提出的YOLO v5l-Im模型的mAP达到了92.1%,较原模型的85.5%提升了6.5个百分点。由此可见,所做的改进有效增强了YOLO v5l对排水管道缺陷的检测能力。展开更多
从 CAP1400核电钢制安全壳和模块整体出厂的构想尚存在诸多不确定因素切入，按照模块整体出厂参数，依据已审批的3000～5000 t 级核电站码头初步设计资料、SG 浮吊卸货运输方案、拟采用18000 t 级半潜船运输构想、重件道路等场内设施初...从 CAP1400核电钢制安全壳和模块整体出厂的构想尚存在诸多不确定因素切入，按照模块整体出厂参数，依据已审批的3000～5000 t 级核电站码头初步设计资料、SG 浮吊卸货运输方案、拟采用18000 t 级半潜船运输构想、重件道路等场内设施初步设计参数，对码头、场内设施等设计方案按照理想状态同比例放大航道、港池、码头、重件道路等参数建立模型，进行模块整体出厂的改造分析。经过对船型参数、航道、港池、码头、场内设施详细的技术与经济对比分析，对模块整体出厂从理论、安全、技术、经济等方面给出了切合实际的建议。展开更多
基金funded by Guizhou Optoelectronic Information and Intelligent Application International Joint Research Center(Qiankehe Platform Talents No.5802[2019]).
文摘Safety helmets can reduce head injuries from object impacts and lower the probability of safety accidents,as well as being of great significance to construction safety.However,for a variety of reasons,construction workers nowadays may not strictly enforce the rules of wearing safety helmets.In order to strengthen the safety of construction site,the traditional practice is to manage it through methods such as regular inspections by safety officers,but the cost is high and the effect is poor.With the popularization and application of construction site video monitoring,manual video monitoring has been realized for management,but the monitors need to be on duty at all times,and thus are prone to negligence.Therefore,this study establishes a lightweight model YOLO_CA based on YOLOv5 for the automatic detection of construction workers’helmet wearing,which overcomes the shortcomings of the current manual monitoring methods that are inefficient and expensive.The coordinate attention(CA)addition to the YOLOv5 backbone strengthens detection accuracy in complex scenes by extracting critical information and suppressing non-critical information.Further parameter compression with deeply separable convolution(DWConv).In addition,to improve the feature representation speed,we swap out C3 with a Ghost module,which decreases the floating-point operations needed for feature channel fusion,and CIOU_Loss was substituted with EIOU_Loss to enhance the algorithm’s localization accuracy.Therefore,the original model needs to be improved so as to enhance the detection of safety helmets.The experimental results show that the YOLO_CA model achieves good results in all indicators compared with the mainstream model.Compared with the original model,the mAP value of the optimized model increased by 1.13%,GFLOPs cut down by 17.5%,and there is a 6.84%decrease in the total model parameters,furthermore,the weight size cuts down by 4.26%,FPS increased by 39.58%,and the detection effect and model size of this model can meet the requirements of lightweight embedding.
文摘针对YOLO v5l(you only look once version 5 large)算法对于小目标、少样本且背景复杂的排水管道缺陷图像检测的精度低、误检和漏检率较高等问题,提出了一种基于YOLO v5l-Im算法的排水管道缺陷检测改进方法。做了三点改进:首先提出了Focal-EIoU(focal embedding intersection over union)损失函数,有效提升了检测模型的性能;其次为增强检测模型对小目标缺陷的检测效果,减少缺陷误检和漏检的概率,将骨干网络中浅层特征图融合到双向特征金字塔网络(bidirectional feature pyramid network,BiFPN)中,增加针对小目标的预测层;最后在YOLO v5l中引入坐标注意力机制(coordinate attention,CA),提高模型对图像中感兴趣区域的敏感程度,减少冗余背景信息的干扰。3种改进对平均检测准确率(mean average precision,mAP)的提升分别为2.0、2.9、5.9个百分点。将三种有效改进融合到一起,检测结果表明:本文提出的YOLO v5l-Im模型的mAP达到了92.1%,较原模型的85.5%提升了6.5个百分点。由此可见,所做的改进有效增强了YOLO v5l对排水管道缺陷的检测能力。
文摘从 CAP1400核电钢制安全壳和模块整体出厂的构想尚存在诸多不确定因素切入，按照模块整体出厂参数，依据已审批的3000～5000 t 级核电站码头初步设计资料、SG 浮吊卸货运输方案、拟采用18000 t 级半潜船运输构想、重件道路等场内设施初步设计参数，对码头、场内设施等设计方案按照理想状态同比例放大航道、港池、码头、重件道路等参数建立模型，进行模块整体出厂的改造分析。经过对船型参数、航道、港池、码头、场内设施详细的技术与经济对比分析，对模块整体出厂从理论、安全、技术、经济等方面给出了切合实际的建议。
