An Effective Method for Underwater Biological Multi-Target Detection Using Mask Region-Based Convolutional Neural Network

Abstract

Underwater creatures play a vital role in maintaining the delicate balance of the ocean ecosystem. In recent years, machine learning methods have been developed to identify underwater biologicals in the complex underwater environment. However, the scarcity and poor quality of underwater biological images present significant challenges to the recognition of underwater biological targets, especially multi-target recognition. To solve these problems, this paper proposed an ensemble method for underwater biological multi-target recognition. First, the CutMix method was improved for underwater biological image augmentation. Second, the white balance, multiscale retinal, and dark channel prior algorithms were combined to enhance the underwater biological image quality, which could largely improve the performance of underwater biological target recognition. Finally, an improved model was proposed for underwater biological multi-target recognition by using a mask region-based convolutional neural network (Mask-RCNN), which was optimized by the soft non-maximum suppression and attention-guided context feature pyramid network algorithms. We achieved 4.97 FPS, the mAP was 0.828, and the proposed methods could adapt well to underwater biological multi-target recognition. The recognition effectiveness of the proposed method was verified on the URPC2018 dataset by comparing it with current state-of-the-art recognition methods including you-only-look-once version 5 (YOLOv5) and the original Mask-RCNN model, where the mAP of the YOLOv5 model was lower. Compared with the original Mask-RCNN model, the mAP of the improved model increased by 3.2% to 82.8% when the FPS was reduced by only 0.38.