arXiv Computer Vision

High-fidelity training-free photovoltaic module mapping from multi-view UAV imagery

The paper introduces MV-PVMapper, a training‑free framework that combines vision foundation models (SAM 3) with UAV photogrammetry to produce high‑fidelity maps of photovoltaic modules. By generating candidate contours, correcting for surface slope, and iteratively refining segmentation across multiple views, the method recovers 7,710 of 7,711 modules with no false positives and achieves macro‑averaged precision and recall above 98 %. The approach outperforms orthomosaic segmentation and multi‑view pixel‑voting baselines, reaching a Jaccard index of 97.55 % without any task‑specific training data.

Hugging Face Trending Papers

Aug 11

Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery

Spatio-temporal PV data are essential for understanding adoption processes in off-grid regions, yet such data remain largely unavailable. Automated segmentation of remote sensing (RS) imagery offers a promising solution; yet, residential PV systems remain challenging targets because of their small size and sparse distribution, resulting in severe target-background imbalance.

llms computer-vision fine-tuning multimodal

Hugging Face Trending Papers

Sep 23

Beyond Balanced Accuracy: A Resolution and Parity-Controlled Benchmark for Vision-Language and Vision-Only Defect Assessment in UAV Power-Line Inspection

Vision-language models (VLMs) are often reported to outperform task-specific vision backbones for unmanned aerial vehicle (UAV) power-line defect assessment. We test that claim on ElecVQA-Bench, a 56,...

llms multimodal benchmarks

arXiv Computer Vision

Sep 24

Beyond Balanced Accuracy: A Resolution and Parity-Controlled Benchmark for Vision-Language and Vision-Only Defect Assessment in UAV Power-Line Inspection

The paper evaluates the claim that vision‑language models (VLMs) outperform task‑specific vision backbones for UAV power‑line defect assessment using the ElecVQA‑Bench benchmark. Across various evaluation settings—partitioning, item sets, label spaces, replication, resolution, and side information—the performance gap between VLMs and traditional backbones is minimal or even reversed when controlling for resolution and token budget. The study concludes that VLM superiority is not universally supported and emphasizes the importance of rigorous benchmark audits.

By Linghao Zhang, Siyu Xiang, Junwei Kuang, Peiyu Yi

llms multimodal benchmarks

arXiv Computer Vision

Sep 22

DroneGround: Open-Vocabulary Drone Payload Characterization Using Synthetic Data and Grounded Vision-Language Models

arXiv:2609.07780v2 Announce Type: replace Abstract: Automated drone surveillance has become increasingly important for public safety, critical infrastructure protection,and restricted airspace monito...

By Ami Pandat, Rajasekhar Punna, Gopika Vinod, Rohit Shukla

llms computer-vision robotics fine-tuning multimodal safety

arXiv Machine Learning

Aug 18

CrevasseSeg: A Label-Efficient UAV Crevasse Segmentation Framework

arXiv:2608. 15790v1 Announce Type: new Abstract: Crevasse mapping from uncrewed aerial vehicle (UAV) imagery matters for glaciological research and for field safety in glaciated terrain.

By Steven Wallace, William D Harcourt, Richard Hann, Aiden Durrant, Somayajulu Sripada, Georgios Leontidis

computer-vision fine-tuning benchmarks safety

arXiv Machine Learning

Aug 10

UAV3DCrop: Benchmarking 3D Reconstruction in Repeated Multi-Angle UAV Crop Surveys

arXiv:2608. 06404v1 Announce Type: cross Abstract: Accurate 3D crop monitoring underpins data-driven precision agriculture by enabling field-scale analysis of plant structure, growth dynamics, and management response.

By Junxiong Zhou, Xuechen Li, Chonghao Qiu, Lang Qiao, Xiaowei Jia, Qi Yang, Chishan Zhang, Leikun Yin, Nanshan You, Vipin Kumar, David Mulla, Ce Yang, Zhenong Jin, Licheng Liu

benchmarks safety

arXiv AI

Jun 29

SpatialUAV: Benchmarking Spatial Intelligence for Low-Altitude UAV Perception, Collaboration, and Motion

arXiv:2606. 27876v1 Announce Type: cross Abstract: Spatial intelligence is essential for low-altitude unmanned aerial vehicle (UAV) perception, collaboration, and navigation.

By Haoyu Zhang, Meng Liu, Qianlong Xiang, Kun Wang, Yaowei Wang, Liqiang Nie

llms multimodal benchmarks

arXiv Computer Vision

5d ago

Drone-PUR: From Visual Clue Discovery to Understanding and Reasoning in UAV Scenes

Drone-PUR is a new dataset and benchmark for vision‑language models that focuses on visual clue discovery, interpretation, and integration in UAV scenes. It contains 11 tasks with 175k training and 20k test samples across single images, image sequences, cross‑modal images, and multiple viewpoints, all of which are human‑reviewed. Evaluation of 22 general‑purpose VLMs shows persistent gaps in spatial judgment and cross‑modal/cross‑view reasoning, while fine‑tuning on Drone‑PUR improves accuracy by up to 18 percentage points.

By Jiahe Wu, Bing Cao, Xingyue Guo, Ruipu Zhao, Beiming Yu, Jiekang Feng, Qinghua Hu, Pengfei Zhu

llms robotics fine-tuning multimodal benchmarks

arXiv AI

Aug 13

Advancing MLLM-based UAV Image Understanding and Reasoning: A Benchmark and a Training-Free Multi-Agent System

arXiv:2608. 11738v1 Announce Type: cross Abstract: Multimodal Large Language Model (MLLM)-based UAV aerial image understanding and reasoning is essential for aerial intelligence yet poses distinct challenges arising from extreme scale variation, arbitrary camera orientations, and high object density.

By Haoyu Zhang, Shuoxun Zhang, Peng Ye, Lin Zhang, Jiakang Yuan, Shenghong Yi, Yuening Wang, Tao Chen

llms agents multimodal benchmarks

arXiv AI

Jul 14

VehAnchor: Metadata-Free Metric Scale Recovery from Vehicle Cues in Aerial Imagery

arXiv:2603. 04277v2 Announce Type: replace-cross Abstract: Autonomous aerial robots operating in GPS-denied or communication-degraded environments frequently lose access to camera metadata and telemetry, leaving onboard perception systems unable to recover the absolute metric scale of the scene.

By Yifei Chen, Chenqian Le, Jiayi Cheng, Xupeng Chen

llms agents computer-vision robotics multimodal benchmarks safety

arXiv AI

Oct 27

DRA-SAM: Disagreement-Driven Referential Active SAM for Low-Altitude Drone Referring Segmentation

arXiv:2610.33057v1 Announce Type: cross Abstract: Referring segmentation in low-altitude drone imagery requires distinguishing small, visually similar instances under complex linguistic and spatial c...

By Jisheng Dang, Huaiyuan Peng, Wencan Zhang, Bimei Wang, Jingwen Zhao, Wenjun Ma, Hong Peng, Bin Hu, Qi Tian, Tat-Seng Chua

computer-vision robotics benchmarks

arXiv Computer Vision

Sep 17

RIM: A Retrieval-In-Matching Framework for Cross-Domain Global Visual Localization of UAVs

arXiv:2607.20116v2 Announce Type: replace Abstract: Global visual localization of unmanned aerial vehicles (UAVs) using remote-sensing reference maps has attracted increasing attention. However, diff...

By Xin Li, Siyuan Duan, Shang Wang, Zhimin Mao, Bingliang Hu, Geng Zhang

computer-vision

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