ABiFN: Attention-based bi-modal fusion network for object detection at night time

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.