# A Novel Multi-layer Framework for Tiny Obstacle Discovery

arXiv: https://arxiv.org/abs/1904.10161 · HTML: https://mlsanity.com/paper/1904.10161/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2019.

## Built on by (most cited first, 14 of 14)

- [Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown Objects](https://mlsanity.com/paper/2303.13769/) (2023)
- [Fast Road Segmentation via Uncertainty-aware Symmetric Network](https://mlsanity.com/paper/2203.04537/) (2022)
- [A Survey of Visual Sensory Anomaly Detection](https://mlsanity.com/paper/2202.07006/) (2022)
- [Video Text Tracking With a Spatio-Temporal Complementary Model](https://mlsanity.com/paper/2111.04987/) (2021)
- [Tiny Obstacle Discovery by Occlusion-Aware Multilayer Regression](https://mlsanity.com/paper/2111.09204/) (2021)
- [AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios](https://mlsanity.com/paper/2410.14379/) (2024)
- [Indoor Obstacle Discovery on Reflective Ground via Monocular Camera](https://mlsanity.com/paper/2401.01445/) (2024)
- [Exploiting Low-level Representations for Ultra-Fast Road Segmentation](https://mlsanity.com/paper/2402.02430/) (2024)
- [Monocular Depth Distribution Alignment with Low Computation](https://mlsanity.com/paper/2203.04538/) (2022)
- [SM4Depth: Seamless Monocular Metric Depth Estimation across Multiple Cameras and Scenes by One Model](https://mlsanity.com/paper/2403.08556/) (2024)
- [Cues3D: Unleashing the Power of Sole NeRF for Consistent and Unique Instances in Open-Vocabulary 3D Panoptic Segmentation](https://mlsanity.com/paper/2505.00378/) (2025)
- [A Feedback-Control Framework for Efficient Dataset Collection from In-Vehicle Data Streams](https://mlsanity.com/paper/2511.03239/) (2025)
- [MARF: Multiscale Adaptive-switch Random Forest for Leg Detection with 2D Laser Scanners](https://mlsanity.com/paper/2204.06833/) (2022)
- [From Big Data to Fast Data: Towards High-Quality Datasets for Machine Learning Applications from Closed-Loop Data Collection](https://mlsanity.com/paper/2603.29474/) (2026)

## Abstract (arXiv)

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.