Fish detection based on Gather-and-Distribute mechanism Multi-scale feature fusion network and Structural Re-parameterization method

Abstract

To solve the problems of localization and identification of fish in the complex fishway environment, improving the accuracy of fish detection, this paper proposes an object detection algorithm YOLORG, and a fishway fish detection dataset (FFDD). The FFDD contains 4,591 images from the web and lab shots and labeled with the LabelIMG tool, covering fish in a wide range of complex scenarios. The YOLORG algorithm, based on YOLOv8, improves the traditional FPN–PAN network into a C2f Multi-scale feature fusion network with a Gather-and-Distribute mechanism, which solves the problem of information loss accompanied by the network in the fusion of feature maps of different sizes. Also, we propose a C2D Structural Re-parameterization module with a rich gradient flow and good performance to further improve the detection accuracy of the algorithm. The experimental results show that the YOLORG algorithm improves the mAP50 and mAP50-95 by 1.2 and 1.8% compared to the original network under the joint VOC dataset, and also performs very well in terms of accuracy compared to other state-of-the-art object detection algorithms, and is able to detect fish in very turbid environments after training on the FFDD. HIGHLIGHTS We propose an FFDD fish detection dataset.; We propose a Structural Re-parameterization convolution module C2D.; We propose a C2f Multi-scale feature fusion network to solve the problem of information loss in the YOLOv8 network.; We propose a YOLORG-series model constructed by C2D Structural Re-parameterization module and C2f Multi-scale feature fusion network.; The proposed method has fewer parameters.;

Keywords

• fish detection in fishway
• multi-scale feature fusion network
• object detection
• structural re-parameterization