Small Sample Underwater Target Recognition Based on Mobilenet_ YOLOV4 Algorithm

Abstract

Objectives: Underwater target recognition through simulation robot, or manual acquisition of seabed image data, the cost of sampling is high, the sample data obtained is limited, and the image quality is poor, and the data can be used for training is small. Methods: Aiming at this problem, this paper improves the algorithm based on yoov4, modifies its feature extraction backbone network, and proposes three kinds of YOLOV4 algorithms based on different Mobile net backbone networks to test the underwater target recognition in the case of small samples. In this paper, the real image of the seabed is used as the original data for training, and the data which is different from the training set is used for prediction. Result: Compared with the original YOLOV4 algorithm under the same conditions, the experimental results of MobilenetV1_YOLOV4 algorithm has the best MAP(86.04%) and FPS(52); and the histogram equalization method is used to enhance the image, which can be used as a further supplementary recognition of the missed target, and reduce the missed rate. Conclusions: The algorithm takes into account both lightweight and accuracy, and provides support for underwater target recognition in marine operation development and aquaculture