arXiv Computer Vision
Oct 22

Early versus Late RGB-Infrared Fusion for Counter-UAV Detection: A Comparative Study

This study compares two fusion strategies for UAV detection using RGB and infrared imagery: early (image-level) fusion and late (decision-level) fusion. Extensive testing in varied lighting, backgrounds, distances, altitudes, and zoom levels shows that early fusion yields higher raw performance by integrating thermal data from the start, while late fusion offers a flexible workflow useful when sensors or frames are misaligned. The paper discusses both strengths and weaknesses of each approach and provides practical implementation recommendations.

By Alberto De Zan, Denis Tavaris, Gian Luca Foresti, Ivan Scagnetto
arXiv Computer Vision
Sep 2

Multimodal RGB-Infrared Combination for UAV-Based Wildfire Segmentation: A Comparative Study on FLAME3

The paper examines RGB‑infrared fusion for binary wildfire segmentation using UAV imagery on the FLAME3 dataset. It compares RGB and infrared baselines with three fusion strategies across U‑Net, DeepLabV3+, and SegFormer architectures. Results show thermal data dominates segmentation performance, and feature‑level fusion with transformer‑based models yields the best results.

By Matheus F. Kovaleski, Lu\'is Garrote, Cristiano Premebida, J\'er\^ome Mendes, Jo\~ao Ruivo Paulo
arXiv Computer Vision
Sep 25

FoCal: Frequency-Oriented Cross-Modal Interaction and Spectral Calibration for Aerial Visible-Infrared Object Detection

FoCal is a new frequency‑oriented framework for aerial RGB–IR object detection that explicitly models cross‑modal interaction across different frequency components. It introduces a Frequency‑Aware Dual‑Domain Calibration module to consolidate low‑frequency structural cues while preserving high‑frequency modality‑specific details, and a Discrepancy‑Guided Spectral Modulation module that adaptively enhances, preserves, or attenuates the joint spectrum based on confidence‑weighted amplitude discrepancies. Experiments on DroneVehicle, ESCVehicle, and ATR‑UMOD show FoCal achieving high mAP scores (83.5%, 54.8%, 64.6%) with only 3.0 M parameters and 113.6 FPS, demonstrating a strong accuracy–efficiency trade‑off.

By Ben Liang, Chao Sui, Junqi Bai, Yuan Liu, Chunlai Li, Xiubao Sui, Qian Chen
arXiv Computer Vision
1d ago

Navigational Safety of Marine Surface Vehicles in Restricted Visibility: From Intelligent Vision to Vision-Centered Multi-Sensor Fusion

arXiv:2611.05818v1 Announce Type: new Abstract: Marine surface vehicles (MSVs) have attracted considerable attention for their commercial, scientific, and military applications. Owing to their favora...

By Ryan Wen Liu, Fengchuan Song, Tianxiang Wang, Guobei Peng, Youngrong Kim, Xian Zhong, Hooi-Siang Kang, Maohan Liang, Junzhi Yu, Shanliang Yao