USD: Uncertainty-Based One-Phase Learning to Enhance Pseudo-Label Reliability for Semi-Supervised Object Detection

With the ease of accessing large unlabeled datasets, studies on semi-supervised learning for object detection (SSOD) have become increasingly popular. Among these SSOD studies, the pseudo-labeling method significantly depends on the accuracy of the pseudo-labels; thus, inaccurate annotations must be filtered to prevent performance degradation. This study classifies annotation errors that occur in pseudo-labeling methods as false negative (FN) and false positive (FP), and solutions to address each type of error are proposed using uncertainty information obtained through Gaussian modeling. Network performance is improved by preventing the background learning of the FN objects based on the uncertainty of the network output. In addition, based on the uncertainty of the annotations, low-reliability annotations are filtered out, and the learning reflectivity of FP objects is determined. Considering the network performance improvement and training complexity, the proposed method employs one-phase learning, including a single pseudo-label update, to achieve maximum performance with the minimum learning process. Moreover, an algorithm is proposed for an optimal update point search to increase the expected performance improvement. Experiments on the Pascal VOC, COCO, and Cityscapes datasets show that the SSD network improves accuracy by 3.3%, 4.7%, and 4.1%, respectively, with negligible computational complexity compared to the baseline.