Faster R-CNN for marine organisms detection and recognition using data augmentation

Abstract

Recently, Faster Region-based Convolutional Neural Network (Faster R-CNN) has achieved marvelous accomplishment in object detection and recognition. In this paper, Faster R-CNN is applied to marine organisms detection and recognition. However, the training of Faster R-CNN requires a mass of labeled samples which are difficult to obtain for marine organisms. Therefore, three data augmentation methods dedicated to underwater-imaging are proposed. Specifically, the inverse process of underwater image restoration is used to simulate different marine turbulence environments. Perspective transformation is proposed to simulate different views of camera shooting. Illumination synthesis is used to simulate different marine uneven illuminating environments. The performance of each data augmentation method, together with previous frequently used data augmentation methods are evaluated by Faster R-CNN on the real-world underwater dataset, which validate the effectiveness of the proposed methods for marine organisms detection and recognition.