DAFD-net: a domain adaptive feature distillation network for dark object detection based on semi-supervised learning

Object detection represents a fundamental task within the realm of computer vision. However, achieving object detection in the dark is still a substantial challenge due to the low contrast of images and the lack of large-scale labeled dark image datasets. One possible solution is to transfer the knowledge of the trained model from a normal illumination source domain to a dark target domain by developing a domain adaptation method. Therefore, we propose a domain adaptive feature distillation network (DAFD-Net) to improve the accuracy of dark object detection. Specifically, we guide the feature extraction of source and target domains by adopting the knowledge distillation training strategy of the teacher-student model and introduce domain adversarial loss and feature distillation loss to jointly conduct semi-supervised optimal learning of the network. Experimental results demonstrate that our DAFD-Net achieves superior performance on the semi- supervised dark detection task.