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 network 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 detectors with strong independence in SVD-RCNN to satisfy the requirements of co-training. Finally， an adaptive 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 experiments confirmed the effectiveness and universality of the method. The results indicated that the method only needs to manually label 5% of the training data to achieve instance-level detection accuracy comparable to that of fully supervised learning； thus， it is suitable for applications in which intelligent robots must efficiently identify different instance objects.