FRSE-Net: low-illumination object detection network based on feature representation refinement and semantic-aware enhancement

Deep learning-based object detection methods have achieved great performance improvement. However, the current mainstream object detectors focus on normal illumination images, while low-illumination object detection is often ignored. It is still a challenging task to detect objects in low-illumination scenes due to insufficient illumination and low visibility. To address this issue, we propose a low-illumination object detection network based on feature representation refinement and semantic-aware enhancement, called FRSE-Net. There are two key components in the proposed network, including a feature capture module (FCM) and a semantic aggregation module (SAM). First, the FCM is designed to enhance the feature representation of the feature map, thus making the object features more discriminative. This is beneficial to capture more effective feature information for subsequent prediction tasks. Furthermore, the SAM is introduced to enhance the semantic-aware ability of the model in low-light images, which makes the detection network focus on the objects of interest to learn rich semantic information. Finally, the experimental results on two low-light image datasets demonstrate the effectiveness and superiority of the proposed network when compared with other advanced low-illumination detection methods.