Cross-domain Object Detection Based On Domain Adaptation

Driven by deep convolutional neural networks,object detection algorithms have made significant progress,but they still face challenges in practical applications.Ex-isting well-performing object detection algorithms all have strict requirements on train-ing data.They not only need to have good annotation information but also need to be independent and identically distributed with the data obtained in actual applications,otherwise,it will cause serious performance degradation.Meanwhile,the work of la-beling data sets still relies on manual labor.That's means once the application scenario changes or is replaced,the corresponding training data set also needs to be reacquired and labeled.This is undoubtedly.time-consuming,labor-intensive,and inefficient,and it is not conducive to the popularization and application of the algorithm.In response to the above problems,domain adaptation provides a new solution.By transferring the knowledge learned by the model on the existing labeled data set to the unlabeled data set,the model can also have a good performance on the unlabeled data set.In this paper,we focuses on the domain adaptation problem in object detection.The specific research work and results are as follows:(1)Aiming at the problem of inaccurate region proposals,missed detections,and false detections in cross-domain object detection using domain adaptive methods,this paper proposes a domain-invariant region proposal network method based on adversar-ial learning and embeds it in Faster R_CNN object detection model.This method is based on adversarial learning and embeds a domain classification network in the region proposal network,so that,the region proposal network can effectively align the features between different domains and generate equally accurate region proposals in different domains.At the same time,a Double-consistency regularization method is proposed to eliminate the domain offset between domain classification networks at different stages,and further improve the generalization ability of cross-domain object detection.Related experimental results verify the effectiveness of the algorithm in this paper.(2)Aiming at the problem of instability in training and difficulty in obtaining opti-mal solutions for training cross-domain object detection models using adversarial learn-ing,this paper proposes a cross-domain object detection algorithm based on a balanced domain classification network.By analyzing the relationship between the domain clas-sification network and the generative adversarial network,this paper explores the rea-sons for the problems in using adversarial learning to train cross-domain object detec-tion models.Inspired by the alternating iterative training methods commonly used in generative adversarial networks,a balanced domain classification network is proposed.By designing a balanced domain classification network and an appropriate learning rate adjustment strategy,the cross-domain object detection model proposed in this paper can stably converge to a better equilibrium point.The experimental results show that the method proposed in this paper can effectively improve the performance of the cross-domain object detection model.