Semi-supervised instance object detection method based on SVD co-training

Detecting indoor instance objects is useful for various applications. Traditional deep-learning methods require a large number of labeled samples for network training，making them time-consuming and labor-intensive. To address this problem，SVD-RCNN—a semi-supervised instance object detection net⁃ work based on singular value decomposition（SVD）and co-training—is proposed. First，key samples are selected for manual labeling to pre-train SVD-RCNN，to ensure that it acquires more prior knowledge. Second，a convergence，decomposition，and finetuning strategy based on SVD is used to obtain two detec⁃ tors with strong independence in SVD-RCNN to satisfy the requirements of co-training. Finally，an adap⁃ tive self-labeling strategy is used to obtain high-quality self-labeling and detection results. The method was tested on multiple indoor instance datasets. On the GMU dataset，it achieved a mean average precision of 79. 3% with 199 manually labeled samples. This was only 2% lower than that（81. 3%）of Faster RCNN with fully supervised learning，which required labeling 3 851 samples. Ablation studies and a series of ex⁃ periments confirmed the effectiveness and universality of the method. The results indicated that the meth⁃ od only needs to manually label 5% of the training data to achieve instance-level detection accuracy compa⁃ rable to that of fully supervised learning；thus，it is suitable for applications in which intelligent robots must efficiently identify different instance objects. © 2023 Chinese Academy of Sciences. All rights reserved.