Instance Segmentation of Underwater Images by Using Deep Learning

Abstract

Based on deep learning, an underwater image instance segmentation method is proposed. Firstly, in view of the scarcity of underwater related data sets, the size of the data set is expanded by measures including image rotation and flipping, and image generation by a generative adversarial network (GAN). Next, the underwater image data set is finally constructed by manual labeling. Then, in order to solve the problems of color shift, blur and the poor contrast of optical images caused by the complex underwater environment and the attenuation and scattering of light, an underwater image enhancement algorithm is used to first preprocess the data set, and several algorithms are discussed, including multi-scale Retinex (MSRCR) with color recovery, integrated color model (ICM), relative global histogram stretching (RGHS) and unsupervised color correction (UCM), as well as the color shift removal proposed in this work. Specifically, the results indicate that the proposed method can largely increase the segmentation mAP (mean average precision) by 85.7% compared with without the pretreatment method. In addition, based on the characteristics of the constructed underwater dataset, the feature pyramid network (FPN) is improved to some extent, and the preprocessing method is further combined with the improved network for experiments and compared with other neural networks to verify the effectiveness of the proposed method, thus achieving the effect and purpose of improving underwater image instance segmentation and target recognition. The experimental analysis results show that the proposed model can achieve a mAP of 0.245, which is about 1.1 times higher than other target recognition models.