Behavior recognition of Hu sheep contributes to their intensive and intelligent farming.Due to the generally high density of Hu sheep farming,severe occlusion occurs among different behaviors and even among sheep perf...Behavior recognition of Hu sheep contributes to their intensive and intelligent farming.Due to the generally high density of Hu sheep farming,severe occlusion occurs among different behaviors and even among sheep performing the same behavior,leading to missing and false detection issues in existing behavior recognition methods.A high-low frequency aggregated attention and negative sample comprehensive score loss and comprehensive score soft non-maximum suppression-YOLO(HLNC-YOLO)was proposed for identifying the behavior of Hu sheep,addressing the issues of missed and erroneous detections caused by occlusion between Hu sheep in intensive farming.Firstly,images of four typical behaviors-standing,lying,eating,and drinking-were collected from the sheep farm to construct the Hu sheep behavior dataset(HSBD).Next,to solve the occlusion issues,during the training phase,the C2F-HLAtt module was integrated,which combined high-low frequency aggregation attention,into the YOLO v8 Backbone to perceive occluded objects and introduce an auxiliary reversible branch to retain more effective features.Using comprehensive score regression loss(CSLoss)to reduce the scores of suboptimal boxes and enhance the comprehensive scores of occluded object boxes.Finally,the soft comprehensive score non-maximal suppression(Soft-CS-NMS)algorithm filtered prediction boxes during the inferencing.Testing on the HSBD,HLNC-YOLO achieved a mean average precision(mAP@50)of 87.8%,with a memory footprint of 17.4 MB.This represented an improvement of 7.1,2.2,4.6,and 11 percentage points over YOLO v8,YOLO v9,YOLO v10,and Faster R-CNN,respectively.Research indicated that the HLNC-YOLO accurately identified the behavior of Hu sheep in intensive farming and possessed generalization capabilities,providing technical support for smart farming.展开更多
针对井工矿复杂环境下安全帽检测面临的挑战,提出了一种改进的YOLOv8目标检测模型。井工矿环境的特殊性,如光线昏暗、粉尘弥漫、背景复杂及矿工姿态多样性,导致现有检测算法在目标遮挡、小目标识别及恶劣环境条件下的检测性能下降。为...针对井工矿复杂环境下安全帽检测面临的挑战,提出了一种改进的YOLOv8目标检测模型。井工矿环境的特殊性,如光线昏暗、粉尘弥漫、背景复杂及矿工姿态多样性,导致现有检测算法在目标遮挡、小目标识别及恶劣环境条件下的检测性能下降。为解决这些问题,从以下几个方面对YOLOv8模型进行了改进:首先,引入残差块+卷积块注意力模块(Residual Block+Convolutional Block Attention Module,ResBlock+CBAM),通过跳跃连接和注意力机制,显著提升了模型对小目标和遮挡目标的检测能力;其次,设计动态检测头,将注意力机制分解为尺度、空间和任务三个独立维度,增强了模型对目标多样性的适应能力;再次,提出用于边界框回归的损失函数——最小点距离的交并比(Minimum Point Distance based Intersection over Union,MPDIoU),综合考虑边界框的重叠面积、中心点距离以及宽度和高度偏差,优化了边界框回归的精度;最后,构建矿井安全帽检测数据集,包含4300张在复杂井下环境下拍摄的图像,用于模型的训练和验证。试验结果表明,改进后的模型在精确率、召回率、mAP50和mAP50-95等关键评价指标上均取得了显著提升。与当前主流的目标检测算法(如Faster R-CNN、SSD、YOLOXs和YOLOv8)相比,提出的模型在检测精度上大幅领先,同时保持了较低的计算复杂度(GFLOPs为16.9),使该模型更适合在实际矿井环境中实时运行。研究不仅提升了井工矿安全帽检测的准确性和鲁棒性,还为复杂环境下的目标检测任务提供了新的思路和方法,具有实际应用价值。展开更多
文摘Behavior recognition of Hu sheep contributes to their intensive and intelligent farming.Due to the generally high density of Hu sheep farming,severe occlusion occurs among different behaviors and even among sheep performing the same behavior,leading to missing and false detection issues in existing behavior recognition methods.A high-low frequency aggregated attention and negative sample comprehensive score loss and comprehensive score soft non-maximum suppression-YOLO(HLNC-YOLO)was proposed for identifying the behavior of Hu sheep,addressing the issues of missed and erroneous detections caused by occlusion between Hu sheep in intensive farming.Firstly,images of four typical behaviors-standing,lying,eating,and drinking-were collected from the sheep farm to construct the Hu sheep behavior dataset(HSBD).Next,to solve the occlusion issues,during the training phase,the C2F-HLAtt module was integrated,which combined high-low frequency aggregation attention,into the YOLO v8 Backbone to perceive occluded objects and introduce an auxiliary reversible branch to retain more effective features.Using comprehensive score regression loss(CSLoss)to reduce the scores of suboptimal boxes and enhance the comprehensive scores of occluded object boxes.Finally,the soft comprehensive score non-maximal suppression(Soft-CS-NMS)algorithm filtered prediction boxes during the inferencing.Testing on the HSBD,HLNC-YOLO achieved a mean average precision(mAP@50)of 87.8%,with a memory footprint of 17.4 MB.This represented an improvement of 7.1,2.2,4.6,and 11 percentage points over YOLO v8,YOLO v9,YOLO v10,and Faster R-CNN,respectively.Research indicated that the HLNC-YOLO accurately identified the behavior of Hu sheep in intensive farming and possessed generalization capabilities,providing technical support for smart farming.
文摘针对井工矿复杂环境下安全帽检测面临的挑战,提出了一种改进的YOLOv8目标检测模型。井工矿环境的特殊性,如光线昏暗、粉尘弥漫、背景复杂及矿工姿态多样性,导致现有检测算法在目标遮挡、小目标识别及恶劣环境条件下的检测性能下降。为解决这些问题,从以下几个方面对YOLOv8模型进行了改进:首先,引入残差块+卷积块注意力模块(Residual Block+Convolutional Block Attention Module,ResBlock+CBAM),通过跳跃连接和注意力机制,显著提升了模型对小目标和遮挡目标的检测能力;其次,设计动态检测头,将注意力机制分解为尺度、空间和任务三个独立维度,增强了模型对目标多样性的适应能力;再次,提出用于边界框回归的损失函数——最小点距离的交并比(Minimum Point Distance based Intersection over Union,MPDIoU),综合考虑边界框的重叠面积、中心点距离以及宽度和高度偏差,优化了边界框回归的精度;最后,构建矿井安全帽检测数据集,包含4300张在复杂井下环境下拍摄的图像,用于模型的训练和验证。试验结果表明,改进后的模型在精确率、召回率、mAP50和mAP50-95等关键评价指标上均取得了显著提升。与当前主流的目标检测算法(如Faster R-CNN、SSD、YOLOXs和YOLOv8)相比,提出的模型在检测精度上大幅领先,同时保持了较低的计算复杂度(GFLOPs为16.9),使该模型更适合在实际矿井环境中实时运行。研究不仅提升了井工矿安全帽检测的准确性和鲁棒性,还为复杂环境下的目标检测任务提供了新的思路和方法,具有实际应用价值。
