Aiming at the problems of large image translation bias at the pixel-level adaptation,the risk of source-bias discrimination at the feature-level adaptation,and the inability of weakly supervised learning to balance detection accuracy and real-time performance,a diversified domain shifter and pseudo bounding box generator are proposed to gradually adjust the pre-training model.The adaptive cross-domain framework is gradually completed at pixel-level and feature-level.A diversified intermediate domain adjustment detection model is generated from the source domain by a domain shifter to bridge the domain gap and reduce the image translation bias.The intermediate domain is used as the supervised source domain,and the pseudo-labeled image adjustment detection model is generated by combining image-level annotations in the target domain to improve source-bias discrimination.A real-time object detector matching the cross-domain framework is constructed based on SSD algorithm to realize real-time object detection under weakly supervised conditions.The mAP on PASCAL VOC migrated to Clipart1k and other datasets is 0.4%～4.7%better than the existing methods.The detection speed is 32 FPS～47 FPS.This improves the accuracy and meets the requirements of real-time detection,and has better migration detection performance.
维普
万方数据