现代电子技术2026，Vol.49Issue(13)：113-118,6.DOI:10.16652/j.issn.1004-373X.2026.13.017
融合YOLOv10与立体视觉的小目标三维检测
Small object 3D detection via YOLOv10 and stereo vision
摘要
Abstract
In civil infrastructure monitoring,seepage is a major hazard to dam stability.Small objects are notoriously difficult to detect due to their limited scale,blurred boundaries,and complex backgrounds,leading to suboptimal localization accuracy.In view of this,the paper proposes a three-dimensional detection method that integrates a deep learning-based object detection model with stereo vision techniques to improve detection performance and spatial positioning accuracy.On the basis of the original detection network,a cross-scale edge enhancement module is introduced to strengthen boundary feature representation,and a lightweight attention mechanism is incorporated to enhance feature extraction efficiency.Leveraging binocular imagery for disparity estimation,this method facilitates the recovery of spatial coordinates for small objects.Validation was conducted based on actual engineering monitoring data.The results demonstrate that the proposed method achieves competitive performance in terms of average precision(AP),spatial localization error,and inference time,offering good real-time performance and adaptability.The proposed method is applicable to complex engineering scenarios such as dam seepage monitoring.Furthermore,it can be readily extended to related tasks in industrial inspection and automated sensing.关键词
三维目标定位/小目标检测/深度学习模型/立体视觉/图像识别/边缘特征提取/注意力机制/渗流监测Key words
three-dimensional object localization/small object detection/deep learning model/stereo vision/image recognition/edge feature extraction/attention mechanism/seepage monitoring分类
信息技术与安全科学引用本文复制引用
胡齐,刘勇..融合YOLOv10与立体视觉的小目标三维检测[J].现代电子技术,2026,49(13):113-118,6.基金项目
湖北省2023年度重点研发计划项目:基于视觉感知与增强技术的内河船舶导航关键技术研究(2023BAB052) （2023BAB052）
湖北省2024年度国际合作项目:基于数据融合与态势感知的船舶导航关键技术研究(2024EHA005) （2024EHA005）
