Floating object detection using double-labelled domain generalization

Detection of floating objects in complicated aquatic environments has a wide range of applications, but it confronts significant hurdles due to the imbalance in detection accuracy and efficiency, and low domain generalization performance. To address these issues, this study proposes a novel floating object detection method based on double-labelled domain generalization. First, the Single Shot Multibox Detector (SSD) is improved by replacing the backbone network with a lightweight feature extraction network, and dynamic feature pyramid network is introduced to balance accuracy and efficiency. Then, this study initializes the improved SSD network based on the double-labelled data of the source domain, and filters the feature extractor bias, classification bias and location bias using pseudo-labelling and feature projection based on the un-labelled source domain data to minimize the bias to improve the domain generalization performance. The proposed method is trained on a selfconstructed floating object dataset and is compared with state-of-the-art methods based on multiple scenarios. The results show that the proposed method achieves better performance in double-labelled domain generalization and conventional domain generalization tasks compared to other methods, achieving 70.33%, 22.38 f/s and 85.29% and 17.81 f/s in accuracy and speed respectively, satisfying the need for multi-scale floating object detection in complex environments and also alleviating the data labelling problem. This work effectively solves the problem of slow detection of floating objects due to the complex model structure and low generalization ability, and provides support for the rapid detection of floating objects in complex scenarios and the promotion of technology applications.