Open AccessABiFN: Attention‐based bi‐modal fusion network for object detection at night time
Author(s) -
Sai Charan A.,
Jitesh M.,
Chowdhury M.,
Venkataraman H.
Publication year - 2020
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2020.1952
Subject(s) - artificial intelligence , computer vision , computer science , rgb color model , modal , object (grammar) , fusion , object detection , complement (music) , sensor fusion , channel (broadcasting) , domain (mathematical analysis) , real time computing , pattern recognition (psychology) , telecommunications , mathematics , mathematical analysis , linguistics , chemistry , philosophy , biochemistry , complementation , polymer chemistry , gene , phenotype
Camera‐based object detection in low‐light/night‐time conditions is a fundamental problem because of insufficient lighting. So far, a mid‐level fusion of RGB and thermal images is done to complement each other's features. In this work, an attention‐based bi‐modal fusion network is proposed for a better object detection in the thermal domain by integrating a channel‐wise attention module. The experimental results show that the proposed framework improves the mAP by 4.13 points on the FLIR dataset.