Research on Marine Life Detection Algorithm Based on Deep Learning

Abstract

Marine life detection based on deep learning has always been the focus and difficulty in the field of deep learning target detection. There are two main problems. First, underwater imaging often has problems such as low image contrast, blurred image, color shift, and blue-green tint of the background. How to solve these problems in imaging. Second, there are many algorithms in the field of deep learning target detection, how to choose a suitable algorithm for marine life detection. Aiming at the above problems, this paper firstly introduces three algorithms suitable for underwater image enhancement: the method based on dark channel prior, the method based on histogram equalization and the method based on Retinex. Then, six commonly used deep learning target detection algorithms are introduced from two aspects: target detection algorithm based on candidate region proposal and target detection algorithm based on regression: R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, SSD, and each analyzes their advantages and disadvantages. It provides a theoretical and method reference for deep learning-based marine biological detection.