A Semi-Supervised Object Detector Based on Adaptive Weighted Active Learning and Orthogonal Data Augmentation

To efficiently utilize limited resources, this paper proposes a semi-supervised object detection (SSOD) approach based on novel adaptive weighted active learning (AWAL) and orthogonal data augmentation (ODA). An uncertainty sampling framework is applied by adaptively weighting multiple evaluations to annotate the most informative samples for active learning. To further exploit the discriminant potential of unlabeled data, an adaptive weighted loss is introduced to fully mine the unlabeled data, and the normalized uncertainty score is adopted as the loss weight to explore low-score samples for training iterations. Moreover, an ODA operation is performed as pseudo-supervised learning on augmented instances to further capture the modality diversity of complex data distributions. Extensive evaluation and analysis are conducted on the MS-COCO dataset, achieving a mean average precision (mAP) of 35.10 with only 10% of the annotated data. Compared with the existing active learning baselines, the AWAL strategy improves the performance by 1.3% without the ODA. When ODA is incorporated, an additional performance gain of 1.2% is observed. Furthermore, training on the fully annotated MS-COCO with additional unlabeled data, the performance achieved at 43.30 mAP, demonstrating the superiority of the proposed approach.