A Semi-Supervised Object Detection Algorithm Based on Teacher-Student Models with Strong-Weak Heads

Semi-supervised object detection algorithms based on the self-training paradigm produce pseudo bounding boxes with unavoidable noise. We propose a semi-supervised object detection algorithm based on teacher-student models with strong-weak heads to cope with this problem. The strong and weak heads of the teacher model solve the quality measurement problem of pseudo label localization to obtain higher-quality pseudo labels. The strong and weak heads of the student model are decoupled to reduce the negative impact of pseudo label noise on classification and regression. We reach 52.5 mAP (+1.8) on the PASCAL visual object classes (PASCAL VOC) dataset and even up to 53.5 mAP (+3.2) by using Microsoft common objects in context (MS-COCO) train2017 as additional unlabeled data. On the MS-COCO dataset, our method also improves about 1.0 mAP with the experimental configurations of 10% COCO and COCO-full as labeled data.