Abstract: For the existing difficulty that underwater fish can’t be recognized quickly and accurately, a transfer learning method with automatic image enhancement function was proposed. In this method, after the color pattern of fish image switched from RGB to Lab, central peripheral operator was used to calculate the significant value of the entire input image, thereby providing the potential region of the fish target, then the GrabCut algorithm was combined to segment the fish target, and finally the original image of the merged segment was sent to the optimized residual network for training. The recognition results of 23 kinds of fishes show that fixing of the conv1 and conv2 layer parameters of the ResNet-50 pre-trained model on the ImageNet data set and fine-tuning of the high-level parameters can achieve the best recognition results, and on the public Fish4Knowledge data set, the model achieved the highest recognition accuracy, with an average recognition accuracy of 99.63%. The recognition accuracy and time performance of the proposed method are better than those of other convolutional neural network methods both on Fish4Knowledge and Fish30Image data sets, and the recognition accuracy is improved by at least 4.98%. Experiments on multiple data sets demonstrate the validity of the model.

Key words: fish recognition, image recognition, convolutional neural networks, transfer learning, significance test

摘要： 针对水下鱼类无法快速准确识别的难点，提出一种具有图像主体自动增强功能的鱼类迁移学习方法。该方法将鱼类RGB图像转换至Lab颜色空间后，利用中央周边算子计算得到整个输入图像的显著性值，进而提供鱼类目标的潜在区域，并结合GrabCut算法获取鱼类分割图像，最终将融合分割图的原始图像送入优化后的残差网络中进行训练。通过对23种鱼类进行识别试验，结果显示，固定ImageNet数据集上ResNet-50预训练模型的conv1层和conv2层参数，微调高层参数的方法能够取得最好的识别效果，且在公开的Fish4Knowledge数据集上，该模型取得了最高的识别准确率，平均识别精度达到99.63%。与其他卷积神经网络方法的对比结果显示，本方法在Fish4Knowledge和Fish30Image数据集上的识别精度和时间性能均具有较大优势，其中识别准确率至少提升4.98%。多个数据集上的实验验证了模型的有效性。

关键词: 鱼类识别, 图像识别, 卷积神经网络, 迁移学习, 显著性检测