SiamFCA: A new fish single object tracking method based on siamese network with coordinate attention in aquaculture

Abstract

In aquaculture, tracking fish helps to obtain information about their growth, behavior, location and health, etc. However, there are some problems in the process of fish tracking. Firstly, to solve the noise problem of underwater images, we use the contrast limited adaptive histogram equalization (CLAHE) to carry out local histogram equalization in each small block, which increases the image contrast while retaining more details. This operation enhances the contrast of the image while suppressing noise to solve the problem of noisy images. Then, to solve the problems of similarity interference, occlusion and scale variation among fish, we propose a new method for fish single object tracking in aquaculture environments, called SiamFCA, by combining the coordinated attention mechanism (CA). The method is based on the siamese network framework and uses a modified AlexNet to extract the depth features of image pairs. And through the coordinate attention mechanism, the model can learn the relationship between different objects more accurately, which can improve the accuracy and robustness of the model when dealing with complex image scenes. Finally, the correlation operation between classification and regression is performed based on the region proposal network (RPN). By classifying, region suggestions can be labeled as positive or negative samples, and regression can calibrate the position and size of the region proposal to match targets more accurately. The experimental results show that the precision and success rate of the algorithm are 84.2 % and 62.4 %, respectively, which are better than the tracking methods such as SiamRPN and DaSiamRPN, etc. The SiamFCA runs at 83 fps, which meets the demand of real-time fish tracking in aquaculture.