Traditional feature-based image stitching techniques often encounter obstacles when dealing with images lackingunique attributes or suffering from quality degradation. The scarcity of annotated datasets in real-life s...Traditional feature-based image stitching techniques often encounter obstacles when dealing with images lackingunique attributes or suffering from quality degradation. The scarcity of annotated datasets in real-life scenesseverely undermines the reliability of supervised learning methods in image stitching. Furthermore, existing deeplearning architectures designed for image stitching are often too bulky to be deployed on mobile and peripheralcomputing devices. To address these challenges, this study proposes a novel unsupervised image stitching methodbased on the YOLOv8 (You Only Look Once version 8) framework that introduces deep homography networksand attentionmechanisms. Themethodology is partitioned into three distinct stages. The initial stage combines theattention mechanism with a pooling pyramid model to enhance the detection and recognition of compact objectsin images, the task of the deep homography networks module is to estimate the global homography of the inputimages consideringmultiple viewpoints. The second stage involves preliminary stitching of the masks generated inthe initial stage and further enhancement through weighted computation to eliminate common stitching artifacts.The final stage is characterized by adaptive reconstruction and careful refinement of the initial stitching results.Comprehensive experiments acrossmultiple datasets are executed tometiculously assess the proposed model. Ourmethod’s Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) improved by 10.6%and 6%. These experimental results confirm the efficacy and utility of the presented model in this paper.展开更多
针对现有车道检测算法准确性和实时性较难平衡的问题,提出了一种基于多特征融合和窗口搜索的新型车道线检测算法.采用多边形填充方法确定车道线的感兴趣区域(region of interest, ROI),融合车道线的颜色、直方图和梯度特征,以消除ROI中...针对现有车道检测算法准确性和实时性较难平衡的问题,提出了一种基于多特征融合和窗口搜索的新型车道线检测算法.采用多边形填充方法确定车道线的感兴趣区域(region of interest, ROI),融合车道线的颜色、直方图和梯度特征,以消除ROI中的复杂背景.通过单应性变换得到车道线的二值图像,基于其像素密度分布寻找车道线初始位置,以窗口搜索方式提取整个车道线上的所有候选像素点.通过拟合像素点构建车道线数学模型.结果表明:提出的算法具有较高的准确性和实时性,算法对黄色车道线、树木阴影遮挡、光照变化、车道线缺损和地面交通标志干扰具有较好的鲁棒性.展开更多
基金Science and Technology Research Project of the Henan Province(222102240014).
文摘Traditional feature-based image stitching techniques often encounter obstacles when dealing with images lackingunique attributes or suffering from quality degradation. The scarcity of annotated datasets in real-life scenesseverely undermines the reliability of supervised learning methods in image stitching. Furthermore, existing deeplearning architectures designed for image stitching are often too bulky to be deployed on mobile and peripheralcomputing devices. To address these challenges, this study proposes a novel unsupervised image stitching methodbased on the YOLOv8 (You Only Look Once version 8) framework that introduces deep homography networksand attentionmechanisms. Themethodology is partitioned into three distinct stages. The initial stage combines theattention mechanism with a pooling pyramid model to enhance the detection and recognition of compact objectsin images, the task of the deep homography networks module is to estimate the global homography of the inputimages consideringmultiple viewpoints. The second stage involves preliminary stitching of the masks generated inthe initial stage and further enhancement through weighted computation to eliminate common stitching artifacts.The final stage is characterized by adaptive reconstruction and careful refinement of the initial stitching results.Comprehensive experiments acrossmultiple datasets are executed tometiculously assess the proposed model. Ourmethod’s Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) improved by 10.6%and 6%. These experimental results confirm the efficacy and utility of the presented model in this paper.
文摘针对现有车道检测算法准确性和实时性较难平衡的问题,提出了一种基于多特征融合和窗口搜索的新型车道线检测算法.采用多边形填充方法确定车道线的感兴趣区域(region of interest, ROI),融合车道线的颜色、直方图和梯度特征,以消除ROI中的复杂背景.通过单应性变换得到车道线的二值图像,基于其像素密度分布寻找车道线初始位置,以窗口搜索方式提取整个车道线上的所有候选像素点.通过拟合像素点构建车道线数学模型.结果表明:提出的算法具有较高的准确性和实时性,算法对黄色车道线、树木阴影遮挡、光照变化、车道线缺损和地面交通标志干扰具有较好的鲁棒性.
