| 刊名 | Agricultural Biotechnology |
| 作者 | Hanlin XU, Shiyu WU, Guochao DING* |
| 作者单位 | College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China |
| DOI | DOI:10.19759/j.cnki.2164-4993.2025.01.018 |
| 年份 | 2025 |
| 刊期 | 1 |
| 页码 | 77-79 |
| 关键词 | Fish; Group behavior; Behavior recognition; Deep learning; YOLOv10 |
| 摘要 | A common but flawed design in existing CNN architectures is using strided convolutions and/or pooling layer, which will result in the loss of fine-grained feature information, especially for low-resolution images and small objects. In this paper, a new CNN building block named SPD-Conv was used, which completely eliminated stride and pooling operations and replaced them with a space-to-depth convolution and a non-strided convolution. Such new design has the advantage of downsampling feature maps while retaining discriminant feature information. It also represents a general unified method, which can be easily applied to any CNN architectures, and can also be applied to strided conversion and pooling in the same way. |
