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:2609.12521v1 Announce Type: new
Abstract: Unmanned aerial vehicle facade inspection can combine red, green, and blue (RGB) imagery with thermal measurements to screen surface and subsurface ano...
By Yuan Yang, Shulei Li, Haobo Liang
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
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:2608.29235v1 Announce Type: new
Abstract: Anti-UAV perception systems must remain reliable when sensor streams degrade under occlusion, fast motion, or modality-specific failure. Existing multi...
By Sharanda Suttorp, Seyed Sahand Mohammadi Ziabari, Ali Mohammed Mansour Alsahag
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
arXiv:2610.27018v1 Announce Type: new
Abstract: Blurred vehicle contours and low contrast limit detection performance in infrared remote sensing imagery, while existing fusion methods may introduce p...
By Wenjuan Zhang, Xiangrui Peng, Zhen Li, Caimeng Yi, Xuesong Ma, Jianwei Wang, Jie Pan
The paper presents a multi‑campaign UAV thermal image dataset for inert ordnance screening, comprising 5,855 labeled image pairs collected in Tennessee across diverse terrains and seasons. The authors trained YOLOV11l and RT‑DETR‑R50 models on 33 m and 15 m altitude data, achieving automated candidate detection, and provided practical guidelines for future humanitarian mine action surveys. The dataset and models aim to aid screening and prioritization for follow‑up technical surveys or EOD assessment, not to replace clearance operations.
By Chad Melton, PhD., Annabelle Kelton
arXiv:2603.16130v5 Announce Type: replace
Abstract: Overexposure caused by strong daylight and oncoming headlights frequently overwhelms visible sensors, resulting in critical information loss in vis...
By Zhiwei Wang, Defeng He, Li Zhao, Xiaoqin Zhang, Yuxing Li, Edmund Y. Lam
arXiv:2610.07705v1 Announce Type: cross
Abstract: The growing use of unmanned aerial vehicles (UAVs) has increased the importance of image-based UAV detection. Learning-based detectors are trained on...
By Wonbin Son, Gyumum Choi, Junil Seo, Hyungjoon Kim
MIC-BEV is a Transformer-based bird's-eye-view perception framework designed for infrastructure-based multi-camera 3D object detection. It supports a variable number of heterogeneous cameras and employs a graph-enhanced fusion module to adaptively integrate multi-view image features by modeling geometric relationships between cameras and the BEV grid. The authors introduce the M2I synthetic dataset and evaluate MIC-BEV on both M2I and the real-world RoScenes dataset, demonstrating strong detection performance, improved robustness under challenging conditions, and practical feasibility for deployment.
By Yun Zhang, Zhaoliang Zheng, Johnson Liu, Zhiyu Huang, Zewei Zhou, Zonglin Meng, Tianhui Cai, Jiaqi Ma
AVID is a new benchmark dataset for detecting tiny aerial targets, comprising 32,040 RGB‑infrared frame pairs from 115 videos with 35,952 bird and drone annotations at 320×256 resolution. The dataset focuses on very small objects, with 68.1% of targets having a geometric‑mean bounding‑box side of 8–16 pixels and 99.0% meeting the COCO small‑object criterion. It introduces metrics for cross‑modal alignment and an AVID‑C extension that simulates seven families of image degradation at five severity levels to study the impact of scale, degradation, and alignment on detection performance.
By Dharini Raghavan, Vishnu Rao, Sethu Selvi S