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.;