Atrous Pyramid GAN Segmentation Network for Fish Images with High Performance

Electronics

Shuyu Chen¹  Yufei Ren²  Yan Zhang²  Xiaoya Zhou²  Qing Wang²  Dongchen Fan³  Jingxian Lin³  Junqi Fu³ 

[1] College of Engineering, China Agricultural University, Beijing 100083, China;College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China;International College Beijing, China Agricultural University, Beijing 100083, China;

关键词: fish dataset;    atrous pyramid structure;    data enhancement;    generative adversarial networks;    segmentation network;   

DOI  :  10.3390/electronics11060911

来源: DOAJ

【 摘 要 】

With the development of computer science technology, theory and method of image segmentation are widely used in fish discrimination, which plays an important role in improving the efficiency of fisheries sorting and biodiversity studying. However, the existing methods of fish images segmentation are less accurate and inefficient, which is worthy of in-depth exploration. Therefore, this paper proposes an atrous pyramid GAN segmentation network aimed at increasing accuracy and efficiency. This paper introduces an atrous pyramid structure, and the GAN module is added before the CNN backbone in order to augment the dataset. The Atrous pyramid structure first fuses the input and output of the dilated convolutional layer with a small sampling rate and then feeds the fused features into the subsequent dilated convolutional layer with a large sampling rate to obtain dense multiscale contextual information. Thus, by capturing richer contextual information, this structure improves the accuracy of segmentation results. In addition to the aforementioned innovation, various data enhancement methods, such as MixUp, Mosaic, CutMix, and CutOut, are used in this paper to enhance the model’s robustness. This paper also improves the loss function and uses the label smoothing method to prevent model overfitting. The improvement is also tested by extensive ablation experiments. As a result, our model’s F1-score, GA, and MIoU were tested on the validation dataset, reaching 0.961, 0.981, and 0.973, respectively. This experimental result demonstrates that the proposed model outperforms all the other contrast models. Moreover, in order to accelerate the deployment of the encapsulated model on hardware, this paper optimizes the execution time of the matrix multiplication method on Hbird E203 based on Strassen’s algorithm to ensure the efficient operation of the model on this hardware platform.

【 授权许可】

Unknown