基于立体相机的深度与图像融合用于道路障碍物检测
Depth and Image Fusion for Road Obstacle Detection Using Stereo Camera
AI总结：
针对道路障碍物出现时间、形态未知且环境干扰导致ML/DL方法不适用的问题，提出结合RGB图像搜索与带SLIC超像素分割的立体图像检测的深度图像融合方法，经地下停车场实验验证可有效检测追踪小型道路障碍物。
摘要：
本文致力于道路上的物体检测，结合了两种分别基于深度信息利用与立体相机数据视频分析的方法。由于道路上物体的出现时间、尺寸和形状均无法预先得知，因此基于机器学习/深度学习（ML/DL）的方法并不适用。人工照明的变化、不均匀的路面纹理以及物体未知的属性与特征，使得这项任务变得更加复杂。为解决该问题，我们开发了深度与图像融合方法：该方法用基于RGB的方法补充对小型高对比度物体的搜索，同时结合SLIC超像素分割的立体图像方法进行障碍物检测。我们在地下停车场针对静态和低速障碍物开展了实验，证明所开发的技术可成功检测甚至追踪小型物体，这些物体可以是停车基础设施构件、遗落在道路上的物品、车轮、掉落的箱子等。
英文摘要：
This paper is devoted to the detection of objects on a road, performed with a combination of two methods based on both the use of depth information and video analysis of data from a stereo camera. Since neither the time of the appearance of an object on the road, nor its size and shape is known in advance, ML/DL-based approaches are not applicable. The task becomes more complicated due to variations in artificial illumination, inhomogeneous road surface texture, and unknown character and features of the object. To solve this problem we developed the depth and image fusion method that complements a search of small contrast objects by RGB-based method, and obstacle detection by stereo image-based approach with SLIC superpixel segmentation. We conducted experiments with static and low speed obstacles in an underground parking lot and demonstrated the successful work of the developed technique for detecting and even tracking small objects, which can be parking infrastructure objects, things left on the road, wheels, dropped boxes, etc.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
