| 基于深度可分离卷积网络的粘连鱼体识别方法 |
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| 引用本文： | 张璐,李道亮,曹新凯,李文升,田港陆,段青玲.基于深度可分离卷积网络的粘连鱼体识别方法[J].农业工程学报,2021,37(17):160-167. |
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| 作者姓名： | 张璐 李道亮 曹新凯 李文升 田港陆 段青玲 |
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| 作者单位： | 1. 中国农业大学国家数字渔业创新中心，北京100083; 2.中国农业大学信息与电气工程学院，北京 100083; 3. 中国农业大学北京市农业物联网工程技术研究中心，北京 100083;;4. 莱州明波水产有限公司，莱州261400; |
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| 基金项目： | 山东省重大科技创新工程（2019JZZY010703）；国家重点研发计划（2017YFE0122100）；宁波市公益性科技项目（202002N3034） |
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| 摘 要： | 及时准确地识别出养殖区域内的粘连鱼体是实现水产养殖中鱼群计数、养殖密度估算等多种基本养殖操作自动化的关键技术。针对目前粘连鱼体识别方法存在准确率低、普适性差等问题，该研究提出了一种基于深度可分离卷积网络的粘连鱼体识别方法。首先采集鱼群图像数据，采用图像处理技术分割出鱼体连通区域图像，构建粘连鱼体识别数据集；其次构建基于深度可分离卷积网络的粘连鱼体识别模型，采用迁移学习方法训练模型；最后基于训练好的模型实现粘连鱼体的识别。在真实的鱼体图像数据集上进行测试，识别准确率达到99.32%。与基于支持向量机（Support Vector Machine, SVM）和基于反向传递神经网络（Back Propagation Neural Network, BPNN）的机器学习方法相比，准确率分别提高了5.46个百分点和32.29个百分点，具有更好的识别性能，为实现水产养殖自动化、智能化提供支持。
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| 关 键 词： | 水产养殖 图像识别 模型 粘连鱼体 深度学习 迁移学习 |
| 收稿时间： | 2020/10/28 0:00:00 |
| 修稿时间： | 2020/1/12 0:00:00 |
Recognition method for adhesive fish based on depthwise separable convolution network |
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| Zhang Lu,Li Daoliang,Cao Xinkai,Li Wensheng,Tian Ganglu,Duan Qingling.Recognition method for adhesive fish based on depthwise separable convolution network[J].Transactions of the Chinese Society of Agricultural Engineering,2021,37(17):160-167. |
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| Authors: | Zhang Lu Li Daoliang Cao Xinkai Li Wensheng Tian Ganglu Duan Qingling |
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| Institution: | 1. National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China; 2. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; 3. Beijing Engineering and Technology Research Center for the Internet of Things in Agriculture, China Agricultural University, Beijing 100083, China;;4. Laizhou Mingbo Aquatic Products Co., Ltd., Laizhou 261400, China; |
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| Abstract: | Fish living in a three-dimensional water environment exhibits various swimming gestures in aquaculture, thereby resulting in the irregular shapes of fish in the images, such as straight or curved bodies, even curved fishtails and bodies. The adhesion between different fish is also complex and varied significantly. In this study, a new depthwise separable convolution network was proposed to improve the accuracy and universality of recognition for the adhesive fish. Image processing was performed on the collected fish images, and then the images of fish-connected areas were segmented to construct an adhesive fish recognition dataset, with a total of 27 836 images. Firstly, some preprocessing operations were performed on the fish images to enhance image contrast, such as color space conversion, color component extraction, and median filtering. Value (V) component of Hue-Saturation-Value (HSV) color space was extracted to serve as the initial image for subsequent processing. Next, the fish images were segmented using the background subtraction. Finally, the open, close, and small-area noise removal operations were implemented to remove the isolated small dots and burrs. After that, the hole filling operation was then conducted to fill the holes on the surface of fish, further to obtain the fish-connected area image. MobileNet was selected as the classification network to build an adhesive fish recognition model using depthwise separable convolution, while the transfer learning was adopted to train the model. Two training mechanisms of transfer learning were designed and then optimized, where the better one was selected according to the experimental data. Specifically, two training were as follows: 1) To freeze all convolutional layers, and only perform the rough training on the fully connected layer; 2) After the first step, subsequently to unfreeze the convolutional layer, and perform fine-tuning training on all layers. As such, the adhesive fish was recognized using the well-trained model. The collected dataset of adhesive fish recognition was randomly divided into a training set and a test set at a ratio of 9:1. Additionally, both images were contained for the model training and validation at a ratio of 9:1 in the training set. Correspondingly, the proposed model was trained and then verified in two transfer learning at three learning rates. The results showed that the model quickly converged with the best training accuracy of 100%, the best validation accuracy of 99.60%, and the best testing accuracy of 99.32%, when the transfer learning was combined the rough training of the fully connected layer, and fine-tuning training of all layers, where the 0.000 1 learning rate was set. Furthermore, the recognition accuracy was increased by 5.46 percentage points and 32.29 percentage points respectively, compared with the Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN). Consequently, the proposed deep learning can also be expected to better perform for the adhesive fish recognition, while adaptively adjust the features, according to actual data and the objects. The combined depthwise separable convolution network and transfer learning can be used to improve the running speed of model. Therefore, the new model can also be conveniently applied in smart terminals, such as mobile phones, particularly for the recognition of adhesive fish automatically and in real-time. |
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| Keywords: | aquaculture image recognition models adhesive fish deep learning transfer learning |
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