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.

The use of object detection algorithms is becoming increasingly important in autonomous vehicles, and object detection at high accuracy and a fast inference speed is essential for safe autonomous driving. A false positive (FP) from a false localization during autonomous driving can lead to fatal accidents and hinder safe and efficient driving. Therefore, a detection algorithm that can cope with mislocalizations is required in autonomous driving applications. This paper proposes a method for improving the detection accuracy while supporting a real-time operation by modeling the bounding box (bbox) of YOLOv3, which is the most representative of one-stage detectors, with a Gaussian parameter and redesigning the loss function. In addition, this paper proposes a method for predicting the localization uncertainty that indicates the reliability of bbox. By using the predicted localization uncertainty during the detection process, the proposed schemes can significantly reduce the FP and increase the true positive (TP), thereby improving the accuracy. Compared to a conventional YOLOv3, the proposed algorithm, Gaussian YOLOv3, improves the mean average precision (mAP) by 3.09 and 3.5 on the KITTI and Berkeley deep drive (BDD) datasets, respectively. Nevertheless, the proposed algorithm is capable of real-time detection at faster than 42 frames per second (fps) and shows a higher accuracy than previous approaches with a similar fps. Therefore, the proposed algorithm is the most suitable for autonomous driving applications.