基于多阶段特征提取的鱼类识别研究

Research on fish recognition based on multi-stage feature extraction learning

鱼类自动识别在海洋生态学、水产养殖等领域应用广泛。受光照变化、目标相似、遮挡及类别分布不均衡等因素影响,鱼类精准自动识别极具挑战性。提出了一种基于多阶段特征提取网络(Multi-stage Feature Extraction Network,MF-Net)模型进行鱼类识别。该模型首先对图片作弱增强预处理,以提高模型的计算效率;然后采用多阶段卷积特征提取策略,提升模型对鱼类细粒度特征的提取能力;最后通过标签平滑损失计算以缓解数据的不平衡性。为验证模型的性能,构建了一个500类、含32768张图片的鱼类数据集,所建模型在该数据集上的准确率达到86.8%,优于现有的主流目标识别方法。利用公开的蝴蝶数据集对该模型进行泛化性能验证,多组消融实验进一步验证了所提算法的有效性。

Automatic fish recognition is widely used in the fields of marine ecology and aquaculture.Due to factors such as fluctuating illumination,overlapping instances and occlusion,accurate automatic identification of fish is extremely challenging.In order to solve these problems,this paper introduces an innovative Multi-stage Feature Extraction Network(MF-Net)model,which is predicated upon a multi-stage feature extraction paradigm for the domain of automatic fish recognition.The architecture of MF-Net commences with a subtle image enhancement preprocessing step,judiciously designed to augment the computational efficiency of the model.Then the deployment of a multi-stage convolutional feature extraction strategy is applied to improve the model's sensitivity towards the granular features of fish species.In an effort to mitigate issues arising from data imbalance,the model incorporates a long-tail loss computation strategy.To evaluate the efficacy of the proposed MF-Net,the study collects a comprehensive fish dataset encompassing 500 categories including 32768 images.The proposed MF-Net demonstrated a remarkable accuracy of 86.8%on this dataset,thereby outperforming the recognition performance of the existing state-of-the-art target recognition algorithms.Furthermore,the model is tested on a publicly butterfly dataset to verify its generalization performance,and multiple ablation experiments further validate the effectiveness of the proposed algorithm.