Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm,a lightweight multi-category abnormal behavior detection algorithm based on ...Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm,a lightweight multi-category abnormal behavior detection algorithm based on improved YOLOv11n is designed.By integrating multi-head grouped self-attention mechanism and Partial-Conv,a two-way feature grouping fusion module(DFPF)was designed,which carried out effective channel segmentation and fusion strategies to reduce redundant calculations andmemory access.C3K2 module was improved,and then unstructured pruning and feature distillation technologywere used.The algorithmmodel is lightweight,and the feature extraction ability for airborne visual abnormal behavior targets is strengthened,and the computational efficiency of the model is improved.Finally,we test the generalization of the baseline model and the improved model on the VisDrone2019 dataset.The results show that com-pared with the baseline model,the detection accuracy of the final improved model on the airborne visual abnormal behavior dataset is improved from 90.2% to 94.8%,and the model parameters are reduced by 50.9% to meet the detection requirements of high efficiency and high precision.The detection accuracy of the improved model on the Vis-Drone2019 public dataset is 1.3% higher than that of the baseline model,indicating the effectiveness of the improved method in this paper.展开更多
为实现深井钻孔内壁形态的高质量全景可视化,克服传统图像拼接方法在自动化水平、拼接质量及处理效率方面的局限,提出了一种融合YOLOv11s(You Only Look Once)与结构相似性(Structural Similarity Index Method)的智能图像拼接方法。研...为实现深井钻孔内壁形态的高质量全景可视化,克服传统图像拼接方法在自动化水平、拼接质量及处理效率方面的局限,提出了一种融合YOLOv11s(You Only Look Once)与结构相似性(Structural Similarity Index Method)的智能图像拼接方法。研究旨在突破现有技术对人工干预依赖性强、易产生接缝、亮度不均及视觉伪影等瓶颈,提升钻孔内壁图像拼接的精度、连续性与整体效率,为后续钻孔质量评估与爆破设计参数优化提供高保真、高一致性的视觉数据支持。在技术方法上,引入轻量化YOLOv11s目标检测网络,充分利用其深层特征提取能力与多尺度检测优势,精准识别钻孔图像中的圆形边界,自动提取圆心坐标与半径参数,有效克服因镜头畸变、光照不均或局部遮挡引起的定位偏差;随后,基于精确的几何参数进行极坐标变换,将环形内壁区域逐帧展开为矩形图像,保留原始纹理信息的同时构建空间有序的展开图集。在此基础上,创新性地融合SSIM结构相似性度量与滑动窗口匹配策略,通过系统分析相邻展开图像在重叠区域内的亮度、对比度与结构一致性,自适应搜索最优配准位置,实现高效、无缝的图像拼接,最终生成完整内壁环状全景图。试验结果表明,该方法在处理240张图像时,拼接耗时仅为34.98 s,同样实验条件下,相较于传统SIFT特征点匹配方法所需的271.35 s,该方法耗时更短且拼接结果具有更高的视觉连贯性与几何保真度,抑制了接缝错位、亮度跳变和纹理重复等常见问题。创新在于提出了一种自动化拼接流程,融合YOLOv11s与SSIM算法,提升了拼接效率与视觉质量。展开更多
基金supported by y the Applied Research Advancement Project in Engineering University of PAP(WYY202304)Research and Innovation Team Project in Engineering University of PAP(KYTD202306)Funding for postgraduate education and teaching.
文摘Aiming at the problem of imbalance between detection accuracy and algorithm model lightweight in UAV aerial image target detection algorithm,a lightweight multi-category abnormal behavior detection algorithm based on improved YOLOv11n is designed.By integrating multi-head grouped self-attention mechanism and Partial-Conv,a two-way feature grouping fusion module(DFPF)was designed,which carried out effective channel segmentation and fusion strategies to reduce redundant calculations andmemory access.C3K2 module was improved,and then unstructured pruning and feature distillation technologywere used.The algorithmmodel is lightweight,and the feature extraction ability for airborne visual abnormal behavior targets is strengthened,and the computational efficiency of the model is improved.Finally,we test the generalization of the baseline model and the improved model on the VisDrone2019 dataset.The results show that com-pared with the baseline model,the detection accuracy of the final improved model on the airborne visual abnormal behavior dataset is improved from 90.2% to 94.8%,and the model parameters are reduced by 50.9% to meet the detection requirements of high efficiency and high precision.The detection accuracy of the improved model on the Vis-Drone2019 public dataset is 1.3% higher than that of the baseline model,indicating the effectiveness of the improved method in this paper.
文摘为实现深井钻孔内壁形态的高质量全景可视化,克服传统图像拼接方法在自动化水平、拼接质量及处理效率方面的局限,提出了一种融合YOLOv11s(You Only Look Once)与结构相似性(Structural Similarity Index Method)的智能图像拼接方法。研究旨在突破现有技术对人工干预依赖性强、易产生接缝、亮度不均及视觉伪影等瓶颈,提升钻孔内壁图像拼接的精度、连续性与整体效率,为后续钻孔质量评估与爆破设计参数优化提供高保真、高一致性的视觉数据支持。在技术方法上,引入轻量化YOLOv11s目标检测网络,充分利用其深层特征提取能力与多尺度检测优势,精准识别钻孔图像中的圆形边界,自动提取圆心坐标与半径参数,有效克服因镜头畸变、光照不均或局部遮挡引起的定位偏差;随后,基于精确的几何参数进行极坐标变换,将环形内壁区域逐帧展开为矩形图像,保留原始纹理信息的同时构建空间有序的展开图集。在此基础上,创新性地融合SSIM结构相似性度量与滑动窗口匹配策略,通过系统分析相邻展开图像在重叠区域内的亮度、对比度与结构一致性,自适应搜索最优配准位置,实现高效、无缝的图像拼接,最终生成完整内壁环状全景图。试验结果表明,该方法在处理240张图像时,拼接耗时仅为34.98 s,同样实验条件下,相较于传统SIFT特征点匹配方法所需的271.35 s,该方法耗时更短且拼接结果具有更高的视觉连贯性与几何保真度,抑制了接缝错位、亮度跳变和纹理重复等常见问题。创新在于提出了一种自动化拼接流程,融合YOLOv11s与SSIM算法,提升了拼接效率与视觉质量。
