Fish Face Identification Based on Rotated Object Detection: Dataset and Exploration

Abstract

At present, fish farming still uses manual identification methods. With the rapid development of deep learning, the application of computer vision in agriculture and farming to achieve agricultural intelligence has become a current research hotspot. We explored the use of facial recognition in fish. We collected and produced a fish identification dataset with 3412 images and a fish object detection dataset with 2320 images. A rotating box is proposed to detect fish, which avoids the problem where the traditional object detection produces a large number of redundant regions and affects the recognition accuracy. A self-SE module and a fish face recognition network (FFRNet) are proposed to implement the fish face identification task. The experiments proved that our model has an accuracy rate of over 90% and an FPS of 200.

Keywords

• identity recognition
• FFRNet
• self-SE module
• rotating box object detection
• real-time detection