MRN-LOD: Multi-exposure Refinement Network for Low-light Object Detection

Low-light conditions present a myriad of intricacies for object detection, with many existing methods relying primarily on image enhancement before detection. Sometimes, the enhancement methods are unable to improve the detection performance in low-light conditions. In this paper, we present a new Multiexposure refinement network for low-light object detection (MRN-LOD) to avoid the need for enhancement before detection. The MRN-LOD contains: multi-exposure feature extractor, adaptive refinement network, and detection head. The developed multi-exposure feature extractor extracts features from the multi-exposure images generated by the low-light image. We introduced the notion of feature extraction from multi-exposure images for object detection in low light. In addition, we proposed an adaptive refinement network to refine the features of low-light images for better detection performance. The detection head uses the refined features to perform object detection. Extensive experimentation on real -world and synthetic datasets shows the superiority of the proposed MRN-LOD.