Traffic sign detection in real scenarios is challenging due to their complexity and small size,often preventing existing deep learning models from achieving both high accuracy and real-time performance.An improved YOL...Traffic sign detection in real scenarios is challenging due to their complexity and small size,often preventing existing deep learning models from achieving both high accuracy and real-time performance.An improved YOLOv8 model for traffic sign detection is proposed.Firstly,by adding Coordinate Attention(CA)to the Backbone,the model gains location information,improving detection accuracy.Secondly,we also introduce EIoU to the localization function to address the ambiguity in aspect ratio descriptions by calculating the width-height difference based on CIoU.Additionally,Focal Loss is incorporated to balance sample difficulty,enhancing regression accuracy.Finally,the model,YOLOv8-CE(YOLOv8-Coordinate Attention-EIoU),is tested on the Jetson Nano,achieving real-time street scene detection and outperforming the Raspberry Pi 4B.Experimental results show that YOLOv8-CE excels in various complex scenarios,improving mAP by 2.8%over the original YOLOv8.The model size and computational effort remain similar,with the Jetson Nano achieving an inference time of 96 ms,significantly faster than the Raspberry Pi 4B.展开更多
为提高水域鱼类资源监测的自动化程度和实时分析能力,结合YOLOv8X(You only look once version 8-extra large)目标检测模型、ByteTrack(ByteTrack:a strong baseline for multi-object tracking)算法与双频识别声呐(Dual-frequency ide...为提高水域鱼类资源监测的自动化程度和实时分析能力,结合YOLOv8X(You only look once version 8-extra large)目标检测模型、ByteTrack(ByteTrack:a strong baseline for multi-object tracking)算法与双频识别声呐(Dual-frequency identification sonar,DIDSON)数据,开发了1种快速、准确的鱼类目标识别与计数方法。实验结果表明,YOLOv8X与ByteTrack联合方法与传统的Echoview软件识别精度接近(偏差率仅为1.36%),但处理时间显著减少(单条测线从约30 min减少至约3 min),表现出较强的实时处理能力和泛化性能。同时,通过重复实验验证了该方法的稳定性,确认其在不同场景中的可靠性。本研究方法与成果为水域鱼类资源的自动化监测提供了可靠的技术支持,可广泛地应用于大范围高频次的渔业资源监测与管理工作中。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
基金supported by Heilongjiang Provincial Natural Science Foundation of China(LH2023E055)the National Key R&D Program of China(2021YFB2600502).
文摘Traffic sign detection in real scenarios is challenging due to their complexity and small size,often preventing existing deep learning models from achieving both high accuracy and real-time performance.An improved YOLOv8 model for traffic sign detection is proposed.Firstly,by adding Coordinate Attention(CA)to the Backbone,the model gains location information,improving detection accuracy.Secondly,we also introduce EIoU to the localization function to address the ambiguity in aspect ratio descriptions by calculating the width-height difference based on CIoU.Additionally,Focal Loss is incorporated to balance sample difficulty,enhancing regression accuracy.Finally,the model,YOLOv8-CE(YOLOv8-Coordinate Attention-EIoU),is tested on the Jetson Nano,achieving real-time street scene detection and outperforming the Raspberry Pi 4B.Experimental results show that YOLOv8-CE excels in various complex scenarios,improving mAP by 2.8%over the original YOLOv8.The model size and computational effort remain similar,with the Jetson Nano achieving an inference time of 96 ms,significantly faster than the Raspberry Pi 4B.
文摘为提高水域鱼类资源监测的自动化程度和实时分析能力,结合YOLOv8X(You only look once version 8-extra large)目标检测模型、ByteTrack(ByteTrack:a strong baseline for multi-object tracking)算法与双频识别声呐(Dual-frequency identification sonar,DIDSON)数据,开发了1种快速、准确的鱼类目标识别与计数方法。实验结果表明,YOLOv8X与ByteTrack联合方法与传统的Echoview软件识别精度接近(偏差率仅为1.36%),但处理时间显著减少(单条测线从约30 min减少至约3 min),表现出较强的实时处理能力和泛化性能。同时,通过重复实验验证了该方法的稳定性,确认其在不同场景中的可靠性。本研究方法与成果为水域鱼类资源的自动化监测提供了可靠的技术支持,可广泛地应用于大范围高频次的渔业资源监测与管理工作中。
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。
