Research And Implementation Of Unsupervised Domain Adaptive Object Detection

Current object detection models based on deep neural networks are used in all aspects of production and life,but deep learning models rely on a large amount of high-quality labeled data and assume that the distribution of test data and training data is consistent.In real scenarios,the data distribution of training set and test set often has a domain shift problem,and the performance of the model after training will be significantly affected.Therefore,the research on unsupervised domain adaptation is significance.This research aims to adapt the model trained on labeled data to unlabeled data,so that it can also ensure high accuracy for unlabeled data.This paper studies the problems existing in the field of object detection based on unsupervised domain adaptation.1.The traditional object detectors used by the current unsupervised cross-domain object detection algorithms rely on anchor boxes and non-maximum suppression algorithms.These methods require artificially setting hyperparameters and affect the cross-domain performance of the model.This paper proposes a coarse-to-fine end-to-end object detection method CF-DETR.This method fuses the global context information and local information of objects,and innovatively integrates multi-scale information into the end-to-end target detection framework;2.In view of the problem of unstable adversarial training in existing unsupervised cross-domain target detection algorithms,this paper proposes A novel cross-domain detection method based on pseudo-labels,MixTeacher,which includes Mixup-based domain alignment method and pseudo-label distribution alignment method to alleviate the problem of domain shift;3.For this field,the information of papers is scattered and difficult to unify,and the model results cannot be directly Compared with the problem of comparison,this paper designs and implements a lightweight cross-domain object detection visualization system.The system builds web services based on Ant Design Pro and Flask,which can effectively integrate the information of existing papers and visually compare the inference results of existing models.In this paper,the performance of CF-DETR is verified on the recognized target detection data set.The experimental results show that the evaluation indicators of CF-DETR are better than existing end-to-end object detectors,and CF-DETR is more suitable for unsupervised domain than traditional object detectors.This paper also verifies the effectiveness of MixTeacher in multiple domain transfer scenarios in the field of unsupervised domain adaptive object detection.The results show that MixTeacher can further improve the cross-domain object detection capability of CF-DETR.The main evaluation in multiple scenarios indicators are optimal.The visualization system designed and implemented in this paper can interactively compare the inference results of different cross-domain object detection models,and intuitively compare the evaluation indicators of different papers,which is convenient for researchers to observe the model characteristics and improve the algorithm in a targeted manner.It is beneficial to the development of the field of unsupervised domain adaptive object detection.