Abstract Intelligent driving is an active area of research in both industry and academia. In order to overcome the shortcomings of traditional machine vision such as visibility is easily affected by illumination conditions, the blind area of infrared small field of view (FOV) is too large and could not provide depth information, this paper proposes a method for detecting significant obstacles based on ultra-wide FOV long-wave infrared (LWIR) stereo vision system. The stereo vision positioning location with ultra-wide FOV is established by the generalized fisheye camera model. On the basis of analyzing obstacle imaging scale and the structure characteristics of the proposed stereo vision system, a multi-scale salient region detection method based on composite pattern is proposed, and its implementation process is described in detail. Experiment shows that the proposed ultra-wide FOV LWIR stereo vision system is able to detect and locate significant obstacles in ultra-wide FOV and the detection rate of pedestrians and vehicles in real complex street scenes is over 92.6%. At the same time, the relative error of pedestrian positioning with a distance of 5 m to 30 m near the central FOV is between 1.6%-10.3%, and its spatial location ability and advantages are verified. The proposed stereo vision system effectively overcomes the shortcomings of existing vision systems, expands the scope of machine vision, and can be used in the field of assistant driving and intelligent driving.

中文翻译：

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具有超宽 FOV LWIR 立体视觉系统的重要障碍物定位

摘要 智能驾驶是工业界和学术界的一个活跃研究领域。针对传统机器视觉存在的能见度易受光照条件影响、红外小视场（FOV）盲区过大无法提供深度信息等缺点，本文提出了一种检测显着效果的方法。基于超宽视场长波红外（LWIR）立体视觉系统的障碍物。采用广义鱼眼相机模型建立超宽视场立体视觉定位。在分析障碍物成像尺度和所提出立体视觉系统结构特点的基础上，提出了一种基于复合模式的多尺度显着区域检测方法，并详细描述了其实现过程。实验表明，所提出的超宽视场长波立体视觉系统能够检测和定位超宽视场中的重要障碍物，真实复杂街景中行人和车辆的检测率超过92.6%。同时，中心视场附近5m~30m的行人定位相对误差在1.6%-10.3%之间，验证了其空间定位能力和优势。所提出的立体视觉系统有效地克服了现有视觉系统的不足，扩展了机器视觉的范围，可应用于辅助驾驶和智能驾驶领域。