Adaptive Adversarial Self-Training for Semi-Supervised Object Detection in Complex Maritime Scenes

Abstract

Semi-supervised object detection helps to monitor and manage maritime transportation effectively, saving labeling costs. Currently, many semi-supervised object detection methods use a combination of data augmentation and pseudo-label to improve model performance. However, these methods may get into trouble in complex maritime scenes, including occlusion, scale variations and lighting variations, leading to distribution bias between labeled data and unlabeled data and pseudo-label bias. To address these problems, we propose a semi-supervised object detection method in complex maritime scenes based on adaptive adversarial self-training, which provides a teacher–student detection framework to use a robust pseudo-label with data augmentation. The proposed method contains two modules called adversarial distribution discriminator and label adaptive assigner. The adversarial distribution discriminator is proposed to match the distribution between augmented data generated from different data augmentations, while the label adaptive assigner is proposed to reduce the labeling bias for unlabeled data so that the pseudo-label of unlabeled data contributes to the detection performance effectively. Experimental results show that the proposed method achieves a better mean average precision of 91.4%, with only 5% of the labeled samples compared with other semi-supervised object detection methods, and its detection speed is 11.1 frames per second. Experiments also demonstrate that the proposed method improves the detection performance compared with fully supervised detectors.

Keywords

• adaptive adversarial self-training
• complex maritime scenes
• distribution bias
• pseudo-label bias
• semi-supervised object detection