融合深度与图像信息，提升道路小障碍物检测能力
Depth and Image Fusion for Road Obstacle Detection Using Stereo Camera
- 结合立体视觉深度图与RGB图像，利用SLIC超像素分割增强检测
- 在地下停车场实测中成功识别并跟踪小尺寸障碍物
- 适合自动驾驶中复杂光照与纹理下的道路障碍感知
本文针对道路障碍物检测问题，提出一种融合深度信息与视频分析的双模方法。由于障碍物出现时间、大小和形状未知，传统机器学习/深度学习方法不适用。受人工照明变化、路面纹理不均及物体特征未知等因素影响，检测难度加大。为此，我们开发了深度与图像融合方法，通过RGB方法补充小对比度目标检测，结合立体图像分析与SLIC超像素分割实现障碍物识别。在地下停车场对静态及低速障碍物进行实验，验证了该技术可有效检测甚至追踪小物体，如停车设施部件、遗落物品、车轮、掉落箱子等。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。