AI RESEARCH
EDFNet: Early Fusion of Edge and Depth for Thin-Obstacle Segmentation in UAV Navigation
arXiv CS.CV
•
ArXi:2604.09694v1 Announce Type: new Autonomous Unmanned Aerial Vehicles (UAVs) must reliably detect thin obstacles such as wires, poles, and branches to navigate safely in real-world environments. These structures remain difficult to perceive because they occupy few pixels, often exhibit weak visual contrast, and are strongly affected by class imbalance. Existing segmentation methods primarily target coarser obstacles and do not fully exploit the complementary multimodal cues needed for thin-structure perception.