mlsanityData to 14 Oct 2026

UCorr: Wire Detection and Depth Estimation for Autonomous Drones

arXiv 2509.14989 ↗First posted 18 Sep 2025cs.CVcited by 0

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Abstract (arXiv)

In the realm of fully autonomous drones, the accurate detection of obstacles is paramount to ensure safe navigation and prevent collisions. Among these challenges, the detection of wires stands out due to their slender profile, which poses a unique and intricate problem. To address this issue, we present an innovative solution in the form of a monocular end-to-end model for wire segmentation and depth estimation. Our approach leverages a temporal correlation layer trained on synthetic data, providing the model with the ability to effectively tackle the complex joint task of wire detection and depth estimation. We demonstrate the superiority of our proposed method over existing competitive approaches in the joint task of wire detection and depth estimation. Our results underscore the potential of our model to enhance the safety and precision of autonomous drones, shedding light on its promising applications in real-world scenarios.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 44 references that are in the map. First posted on arXiv 18 Sep 2025.

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17 of 44 references.

References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,274
  5. 2015FlowNet: Learning Optical Flow with Convolutional Networkscited by 1,059
  6. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 820
  7. 2023ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depthcited by 657
  8. 2020AdaBins: Depth Estimation using Adaptive Binscited by 601
  9. 2014Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecturecited by 483
  10. 2016Deeper Depth Prediction with Fully Convolutional Residual Networkscited by 403
  11. 2022BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimationcited by 168
  12. 2016Richer Convolutional Features for Edge Detectioncited by 143
5 more
  1. 2023Understanding the Role of the Projector in Knowledge Distillationcited by 31
  2. 2023DDOS: The Drone Depth and Obstacle Segmentation Datasetcited by 10
  3. 2019LS-Net: Fast Single-Shot Line-Segment Detectorcited by 5
  4. 2023Automatic High Resolution Wire Segmentation and Removalcited by 4
  5. 2017Fast, Accurate Thin-Structure Obstacle Detection for Autonomous Mobile Robotscited by 1

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