Research And Implementation Of Object Detection System Based On Domain Adaptation

With the development of deep learning,object detection is more and more widely used.However,it still relies heavily on manual tagging and has low scene generalization ability at present.Domain adaptive object detection technology is committed to making the model maintain good performance without tagging when transferring to different domains.However,most of the current domain adaptive object detection methods are based on two-stage object detection methods,which are not completely suitable for one-stage detection model.And there are still some problems in the existing methods:(1)Most of the current style transfer methods use the cycle-consistency loss to carry out bidirectional transfer of scenes,which is difficult to deal with the degradation of transfer effect in nonbidirectional mapping scenarios,thus reducing the cross-domain ability of the model in such scenarios;(2)The current methods based on adversarial training all adopt the strategy of dual-path single image input strategy,which disables all batch normalization layers,thus increasing the training difficulty of cross-domain models and limiting their detection precision.In view of the above problems,this paper studies the following contents based on the one-stage object detection model:(1)A semisupervised cross-domain object detection algorithm based on style transfer was proposed.The one-way transfer model based on contrastive learning was adopted to improve the effect of style transfer,pseudo-labeling and multi-stage three-domain mix training are combined to improve the crossdomain capability of the model,which improved the average precision of the model by 6.3%in the day-to-night task.(2)An adversarial-based domain adaptive object detection algorithm is proposed,which adopts the learning strategy based on alternating input of different domain batches and combines the proposed domain weighted learning and domain mixed learning to make the model achieve the state-of-the-art performance on multiple datasets.Based on the above two algorithm models,a domain adaptive object detection system was designed and implemented.The algorithm was deployed in the ground,and an interactive interface was designed so that users could submit new datasets in reality for cross-domain adaptation,which verified the cross-domain detection ability of the model in real tasks...