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Deep learning-based automatic recognition network of agricultural machinery images

文献类型： 外文期刊

作者： Zhang, Ziqiang 1 ; Liu, Hui 1 ; Meng, Zhijun 2 ; Chen, Jingping 2 ;

作者机构： 1.Capital Normal Univ, Informat Engn Coll, Beijing 100048, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Beijing Nongke Mansion Room B-508, Beijing 100097, Peoples R China

关键词： Agricultural machinery images; Deep learning; Image recognition; Inception_v3 network; AMTNet

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2019 年 166 卷

页码：

收录情况： SCI

摘要： Due to the massive amount of data generated by the mobile Internet and the development of large-scale computing devices and technologies, the deep learning algorithm has experienced a breakthrough in terms of image recognition technology. Traditional image recognition requires the complex extraction of image features, whereas deep learning technology can automatically learn image features through multi-layer nonlinear transformation, which is especially proficient at extracting complex global features. An image annotation dataset containing the images of seven types of machines and six types of abnormal images was constructed in this study from the large number of machine images in the agricultural machinery operation supervisory service system. To improve the Inception_v3 network, a network called AMTNet was designed and trained for automatic recognition of agricultural machinery images. Under the same experimental conditions, AMTNet achieved recognition accuracies of 97.83% and 100% on validation sets Top _1 and Top_5, respectively, demonstrating better performance than the classic networks ResNet_50 and Inception v3. To further test the performance of AMTNet, 200 images of each of the 13 types of machine images were selected as test sets. The average area under the curve and Fl-score of the network for image recognition of various machines reached 92% and 96%, respectively. According to the test results, AMTNet shows good robustness to illumination, environmental changes, and small area occlusion, which meets the practical application requirements of intelligent supervision over agricultural machinery operation.

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