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.

中文翻译：

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ABiFN：用于夜间目标检测的基于注意力的双峰融合网络

由于光线不足，在弱光/夜间条件下基于摄像机的物体检测是一个基本问题。到目前为止，已经完成了RGB和热图像的中间融合，以补充彼此的功能。在这项工作中，提出了一种基于注意力的双峰融合网络，通过集成基于通道的注意力模块，可以更好地在热域中进行物体检测。实验结果表明，所提出的框架将FLIR数据集的mAP提高了4.13点。