COXNet: Cross-Layer Fusion With Adaptive Alignment and Scale Integration for RGBT Tiny Object Detection

TY - JOUR

T1 - COXNet

T2 - Cross-Layer Fusion With Adaptive Alignment and Scale Integration for RGBT Tiny Object Detection

AU - Peng, Peiran

AU - Xu, Tingfa

AU - Song, Liqiang

AU - Zhu, Mengqi

AU - Fang, Yuqiang

AU - Li, Jianan

N1 - Publisher Copyright: © 1991-2012 IEEE.

PY - 2026/1

Y1 - 2026/1

N2 - Detecting tiny objects in multimodal Red-Green-Blue-Thermal (RGBT) imagery is a critical challenge in computer vision, particularly in surveillance, search and rescue, and autonomous navigation. Drone-based scenarios exacerbate these challenges due to spatial misalignment, low-light conditions, occlusion, and cluttered backgrounds. Current methods struggle to leverage the complementary information between visible and thermal modalities effectively. We propose COXNet, a novel framework for RGBT tiny object detection, addressing these issues through three core innovations: i) the Cross-Layer Fusion Module, fusing high-level visible and low-level thermal features for enhanced semantic and spatial accuracy; ii) the Dynamic Alignment and Scale Refinement module, correcting cross-modal spatial misalignments and preserving multi-scale features; and iii) an optimized label assignment strategy using the GeoShape Similarity Measure for better localization. COXNet achieves a 3.32% mAP50 improvement on the RGBTDronePerson dataset over state-of-the-art methods, demonstrating its effectiveness for robust detection in complex environments.

AB - Detecting tiny objects in multimodal Red-Green-Blue-Thermal (RGBT) imagery is a critical challenge in computer vision, particularly in surveillance, search and rescue, and autonomous navigation. Drone-based scenarios exacerbate these challenges due to spatial misalignment, low-light conditions, occlusion, and cluttered backgrounds. Current methods struggle to leverage the complementary information between visible and thermal modalities effectively. We propose COXNet, a novel framework for RGBT tiny object detection, addressing these issues through three core innovations: i) the Cross-Layer Fusion Module, fusing high-level visible and low-level thermal features for enhanced semantic and spatial accuracy; ii) the Dynamic Alignment and Scale Refinement module, correcting cross-modal spatial misalignments and preserving multi-scale features; and iii) an optimized label assignment strategy using the GeoShape Similarity Measure for better localization. COXNet achieves a 3.32% mAP50 improvement on the RGBTDronePerson dataset over state-of-the-art methods, demonstrating its effectiveness for robust detection in complex environments.

KW - RGBT imagery

KW - multimodal fusion

KW - spatial misalignment correction

KW - tiny object detection

UR - https://www.scopus.com/pages/publications/105013057407

U2 - 10.1109/TCSVT.2025.3595147

DO - 10.1109/TCSVT.2025.3595147

M3 - Article

AN - SCOPUS:105013057407

SN - 1051-8215

VL - 36

SP - 596

EP - 608

JO - IEEE Transactions on Circuits and Systems for Video Technology

JF - IEEE Transactions on Circuits and Systems for Video Technology

IS - 1

ER -