SOMC:A Object-Level Data Augmentation for Sea Surface Object Detection. Issue 1 (1st January 2022) - Hu, Jianqiu; He, Jiazhou; Jiang, Pan; Yin, Yuwei
Abstract: The deep learning model is a data-driven model and more high-quality data will bring it better results. In the task of Unmanned Surface Vessel's object detection based on optical images or videos, the object is sparser than the target in the natural scene. The current datasets of sea scenes often have some disadvantages such as high image acquisition costs, wide range of changes in object size, imbalance in the number of different objects and so on, which limit the generalization of the model for the detection of sea surface objects. In order to solve problems of insufficient scene and poor effect in current sea surface object detection, an object-level data augmentation for sea surface objects called SOMC is proposed. According to the different scenarios faced by the USV when performing autonomous obstacle avoidance, patrol and other tasks, SOMC generates suitable scenarios by mixing and copying targets conveniently, providing the possibility of unlimited expansion of the sea surface object. The experiment selected images in the video taken by the camera on top of the USV. A sufficient amount of comparative experiment prove that the SOMC integrates with existing excellent data augmentations and achieved an improvement in the detection effect, which proves the effectiveness and practicability of the SOMC in the perception task of the USV.