An adaptive lightweight small object detection method for incremental few-shot scenarios of unmanned surface vehicles

Real-time and accurate detection of sea surface objects has become an important research topic for unmanned surface vehicles (USVs). During the execution of tasks, USVs sometimes need to upgrade their model to detect new categories, and the initial data is often limited. This requires quick adaptation to new categories under few-shot scenarios. We propose a lightweight neural network for detecting small sea surface objects named Shuffle-High-Resolution-Net (SHRDet), which integrates the enhanced Shuffle Block based on High-Resolution-Net (HRNet), lightweight feature fusion module, and Focal Efficient Intersection over Union loss. Based on SHRDet, a fast adaptation method named SHRDet-N for incremental few-shot categories is proposed. It generates category enhancement features through a cross-attention mechanism, and introduces elastic weight consolidation and feature distance to solve catastrophic forgetting when learning incremental few-shot categories. The algorithms have been applied to an intelligent USV platform for various surface missions, such as security patrol, ocean investigation, and marine engineering. The experimental results on public datasets indicate that SHRDet achieves 80.7 % mean Average Precision (mAP) on the Water Surface Object Detection Dataset (WSODD) with only 0.69 M parameters and less calculation quantity, and SHRDet is significantly superior to state-of-the-art methods in terms of lightweight and accuracy. Moreover, SHRDet-N effectively solves the learning problem of incremental few-shot categories. When the sample number of a new category is set to 20, the mAP of the base categories is 82.5 % and that of the new category is 63.8 %, which is 2.8 % and 4.2 % higher than that of state-of-the-art models like Sylph.