Abstract
For tiny obstacle discovery in a monocular image, edge is a fundamental visual element. Nevertheless, because of various reasons, e.g., noise and similar color distribution with background, it is still difficult to detect the edges of tiny obstacles at long distance. In this paper, we propose an obstacle-aware discovery method to recover the missing contours of these obstacles, which helps to obtain obstacle proposals as much as possible. First, by using visual cues in monocular images, several multi-layer regions are elaborately inferred to reveal the distances from the camera. Second, several novel obstacle-aware occlusion edge maps are constructed to well capture the contours of tiny obstacles, which combines cues from each layer. Third, to ensure the existence of the tiny obstacle proposals, the maps from all layers are used for proposals extraction. Finally, based on these proposals containing tiny obstacles, a novel obstacle-aware regressor is proposed to generate an obstacle occupied probability map with high confidence. The convincing experimental results with comparisons on the Lost and Found dataset demonstrate the effectiveness of our approach, achieving around 9.5% improvement on the accuracy than FPHT and PHT, it even gets comparable performance to MergeNet. Moreover, our method outperforms the state-of-the-art algorithms and significantly improves the discovery ability for tiny obstacles at long distance.
Abstract (translated)
对于单眼图像中的微小障碍物发现，边缘是一个基本的视觉元素。然而，由于各种原因，如噪声和与背景颜色分布相似，在远距离很难探测到微小障碍物的边缘。在本文中，我们提出了一种障碍识别方法来恢复这些障碍物的缺失轮廓，这有助于获得尽可能多的障碍物建议。首先，通过在单眼图像中使用视觉提示，精心推断出几个多层区域来显示与相机的距离。其次，构造了几种新的障碍物感知遮挡边缘图，以很好地捕捉微小障碍物的轮廓，这些轮廓结合了来自每一层的线索。第三，为了保证小障碍方案的存在，利用各层地图进行方案提取。最后，基于这些包含微小障碍物的建议，提出了一种新的障碍物感知回归器，以生成一个高置信度的障碍物占用概率图。通过对失物招领数据集的比较，令人信服的实验结果证明了我们的方法的有效性，与fpht和pht相比，精度提高了9.5%，甚至获得了与mergenet相当的性能。此外，我们的方法优于最先进的算法，大大提高了在远距离发现微小障碍物的能力。
URL
https://arxiv.org/abs/1904.10161
