Abstract
At present, humans need to sort fish species manually after landing at fish markets. Since the sorting process is a heavy workload, automation of the process is required. Although fish recognition methods using color images of fish and images of laser-irradiated fish have been proposed, there is currently no method for recognizing fish based on their entire three-dimensional shapes. In this study, we represent 3D shape of entire horse mackerels and mackerels as point clouds and investigate a recognition method using PointNet which is limited to rotation around the Z-axis only. First, we investigated the relations between the number of points used for recognition, accuracy and the recognition speed, and our findings showed that using 128 points resulted in the best recognition performance. Additionally, we compared our method to original PointNet and found that limiting rotation improved recognition. Moreover, we compared the recognition of point clouds, color images and depth images, and found that point clouds recognition had equal or higher than those of color images or depth images.
