Attention-based Bi-modal Fusion Network for Object Detection at Night-time [Paper Link]
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 (ABiFN) is proposed for better object detection in the thermal domain by integrating a channel-wise attention module. The experimental results show our framework improves the mAP by 4.13 points on the FLIR dataset.
Object classes for detection are car, person and bicycle
Dataset used is the FLIR dataset - https://www.flir.in/oem/adas/adas-dataset-form/
Most of the code is used and modified from the https://github.com/jwyang/faster-rcnn.pytorch repository