🤖 AI Summary
Real-time detection of small obstacles (e.g., hubcaps, cardboard boxes) under complex illumination and unstructured road surfaces remains challenging due to low contrast, ambiguous textures, and lack of prior knowledge.
Method: This paper proposes a training-free depth-RGB collaborative detection framework. It fuses stereo-derived depth maps with RGB imagery via a multimodal superpixel fusion mechanism, jointly enhancing SLIC-guided stereo matching and fine-grained texture discrimination for small objects. The approach operates without scene priors or annotated data.
Contribution/Results: The method achieves robust detection and stable tracking of static and low-speed small obstacles of arbitrary size, shape, and appearance time. Evaluated in underground parking lots, it significantly improves recall under low-contrast and dynamically varying artificial lighting conditions. By eliminating reliance on labeled datasets or domain-specific assumptions, it offers a cost-effective, highly adaptive perception solution for autonomous driving systems.
📝 Abstract
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.",论文提出了一种融合深度信息与图像视觉的双摄系统，利用SLIC超像素分割技术在复杂环境下有效检测道路障碍物，特别适用于识别颜色不鲜明的小型障碍，如轮胎、箱子等，实验证明在地下停车场场景下效果显著。,"The depth and image fusion method is developed that complements a search of small contrast objects by RGB-based method, and obstacle detection by stereo image-based approach with SLIC superpixel segmentation.