Registration-Free Hybrid Learning Empowers Simple Multimodal Imaging System for High-quality Fusion Detection

• URL: http://arxiv.org/abs/2307.03425v1
• Date: Fri, 7 Jul 2023 07:11:37 GMT
• Title: Registration-Free Hybrid Learning Empowers Simple Multimodal Imaging System for High-quality Fusion Detection
• Authors: Yinghan Guan, Haoran Dai, Zekuan Yu, Shouyu Wang and Yuanjie Gu
• Abstract summary: We propose IA-VFDnet, a CNN-Transformer hybrid learning framework with a unified high-quality multimodal feature matching module. AKM and DWDAF work in synergy to perform high-quality infrared-aware visible fusion detection. Experiments on the M3FD dataset validate the superiority of the proposed method.
• Score: 1.9249287163937976
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal fusion detection always places high demands on the imaging system and image pre-processing, while either a high-quality pre-registration system or image registration processing is costly. Unfortunately, the existing fusion methods are designed for registered source images, and the fusion of inhomogeneous features, which denotes a pair of features at the same spatial location that expresses different semantic information, cannot achieve satisfactory performance via these methods. As a result, we propose IA-VFDnet, a CNN-Transformer hybrid learning framework with a unified high-quality multimodal feature matching module (AKM) and a fusion module (WDAF), in which AKM and DWDAF work in synergy to perform high-quality infrared-aware visible fusion detection, which can be applied to smoke and wildfire detection. Furthermore, experiments on the M3FD dataset validate the superiority of the proposed method, with IA-VFDnet achieving the best detection performance than other state-of-the-art methods under conventional registered conditions. In addition, the first unregistered multimodal smoke and wildfire detection benchmark is openly available in this letter.

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