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| 水下鱼类品种识别模型与实时识别系统 | |
| 作者姓名： | 李少波 杨玲 于辉辉 陈英义 |
| 作者单位： | 中国农业大学 信息与电气工程学院，北京 100083 中国农业大学国家数字渔业创新中心，北京 100083 北京市农业物联网工程技术研究中心，北京 100083 北京林业大学 信息学院，北京 100083 |
| 摘 要： | 快速准确的鱼类识别系统需要良好的识别模型和部署系统作为支撑。近年来，卷积神经网络在图像识别领域取得了巨大成功，不同的卷积网络模型都有不同的优点和缺点，面对众多可供选择的模型结构，如何选择和评价卷积神经网络模型成为了必须考虑的问题。此外，在模型应用方面，移动终端直接部署深度学习模型需要对模型进行裁剪、压缩处理，影响精度的同时还会导致安装包体积增大，不利于模型升级维护。针对上述问题，本研究根据水下鱼类实时识别任务特点，选取了AlexNet、GoogLeNet、ResNet和DenseNet预训练模型进行对比试验研究，通过在Ground-Truth鱼类公开数据集基础上对图像进行随机翻转、旋转、颜色抖动来增强数据，使用Label smoothing作为损失函数缓解模型过拟合问题，通过研究Ranger优化器和Cosine学习率衰减策略进一步提高模型训练效果。统计各个识别模型在训练集和验证集上的精确度和召回率，最后综合精确度和召回率量化模型识别效果。试验结果表明，基于DenseNet训练的鱼类识别模型综合评分最高，在验证集的精确度和召回率分别达到了99.21%和96.77%，整体F1值达到了0.9742，模型理论识别精度达到预期。基于Python开发并部署了一套远程水下鱼类实时识别系统，将模型部署到远程服务器，移动终端通过网络请求进行鱼类识别模型调用，验证集图像实际测试表明，在网络良好条件下，移动终端可以在1 s内准确识别并显示鱼类信息。 |
| 关 键 词： | 鱼类识别模型 卷积神经网络 模型评价 安卓 Ground-Truth 实时识别系统 |
| 收稿时间： | 2021-09-07 |
Underwater Fish Species Identification Model and Real-Time Identification System |
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| Authors: | LI Shaobo YANG Ling YU Huihui CHEN Yingyi |
| Institution: | College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China Beijing Engineering and Technology Research Centre for the Internet of Things in Agriculture, Beijing 100083, China School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China |
| Abstract: | Convolutional neural network models have different advantages and disadvantages, it is becoming more and more difficult to select an appropriate convolutional neural network model in an actual fish identification project. The identification of underwater fish is a challenge task due to varies in illumination, low contrast, high noise, low resolution and sample imbalance between each type of image from the real underwater environment. In addition, deploying models to mobile devices directly will reduce the accuracy of the model sharply. In order to solve the above problems, Fish Recognition Ground-Truth dataset was used to training model in this study, which is provided by Fish4Knowledge project from University of Edinburgh. It contains 27,370 images with 23 fish species, and has been labeled manually by marine biologists. AlexNet, GoogLeNet, ResNet and DenseNet models were selected initially according to the characteristics of real-time underwater fish identification task, then a comparative experiment was designed to explore the best network model. Random image flipping, rotation and color dithering were used to enhance data based on ground-truth fish dataset in response to the limited number of underwater fish images. Considering that there was a serious imbalance in the number of samples in each category, the label smoothing technology was used to alleviate model overfitting. The Ranger optimizer and Cosine learning rate attenuation strategy were used to further improve the training effect of the models. The accuracy and recall rate information of each model were recorded and counted. The results showed that, the accuracy and recall rate of the fish recognition model based on DenseNet reached 99.21% and 96.77% in train set and validation set respectively, its F1 value reached 0.9742, which was the best model obtained in the experiment. Finally, a remote fish identification system was designed based on Python language, in this system the model was deployed to linux server and the Android APP was responsible for uploading fish images via http to request server to identify the fishes and displaying the identification information returned by server, such as fish species, profiles, habits, distribution, etc. A set of recognition tests were performed on real Android phone and the results showed that in the same local area net the APP could show fish information rapidly and exactly within 1 s. |
| Keywords: | fish identification model CNN model evaluation Android Ground-Truth real-time identification system |
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