A multi-scale features-based method to detect Oplegnathus

Abstract

It is of great significance to use underwater video and image processing technology to detect and analyze fish behaviors. In this paper, an Oplegnathus image dataset for fish behaviors study by deep learning algorithm is constructed, and the data is captured from two cameras (one above water and another below water); and then an improved Neural Network model based on multi-scale features is proposed for fish behaviors learning automatically. To overcome the occlusion and blur problems of the images, the lightweight neural network MobileNet-SSD is improved by adding a dilate convolution, and SE blocks are added to the feature maps at different scales to establish a self-attention mechanism; the Focal Loss function is used to calculate the classification loss and to balance the proportion of background and target samples. The results of the experiments show that the average behaviors detection accuracy of our method reach 90.94% and 88.36% in both overwater and underwater datasets.

Keywords

• Detection of the Oplegnathus
• Deep learning
• MobileNet-SSD
• Neural networks