Domain Diversification Progressive Cross-Domain Weakly-Supervised Real-time Object Detection

Abstract

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.