Nighttime Object Detection Method Based on Domain Adaptation and Category Contrast
In the task of nighttime object detection,the visibility of the object is low,and it is difficult to annotate a large amount of im-age data,which makes it difficult to use supervised methods for large-scale data training.And when using a small-scale annotated data for training supervised methods tend to overfit on the training data,resulting in poor prediction accuracy.To address these issues,an unsupervised domain adaptation nighttime object detection model using labeled daytime images and unlabeled nighttime images for training is proposed in this paper.Day-night image enhancement methods is employed in the model to reduce the domain gap and en-hance the complexity of nighttime training data to enrich feature learning;multi-scale channel attention is introduced to the Faster-RC-NN model to enhance its ability to perceive multi-scale features;and class-level contrastive learning is used to obtain discriminative and domain-invariant class features.Experiments conducted on the urban traffic datasets BDD100K and SODA10M demonstrate that the performance of the proposed method exceeds that of commonly used domain adaptation target detection methods.
NETL
NSTL
万方数据
