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Method for the automatic recognition of cropland headland images based on deep learning

文献类型： 外文期刊

作者： Qiao, Yujie 1 ; Liu, Hui 1 ; Meng, Zhijun 2 ; Chen, Jingping 2 ; Ma, Luyao 1 ;

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.ICT Applicat Precis Agr, 56,N Rd Western 3rd-Ring, Beijing 100048, Peoples R China

4.Intelligent Agriculturalequipment Room A-517,Be, Beijing 100097, Peoples R China

关键词： cropland image; deep learning; image recognition; model compression; MobileNetV2 network

期刊名称：INTERNATIONAL JOURNAL OF AGRICULTURAL AND BIOLOGICAL ENGINEERING （ 影响因子：2.4； 五年影响因子：2.8 ）

ISSN： 1934-6344

年卷期： 2023 年 16 卷 2 期

页码：

收录情况： SCI

摘要： For self-driving agricultural vehicles, the sensing of the headland environment based on image recognition is an important technological aspect. Cropland headland environments are complex and diverse. Traditional image feature extraction methods have many limitations. This study proposed a method of automatic cropland headland image recognition based on deep learning. Based on the characteristics of cropland headland environments and practical application needs, a dataset was constructed containing six categories of annotated cropland headland images and an augmented headland image training set was used to train the compact network MobileNetV2. Under the same experimental conditions, the model prediction accuracy for the first ranked category in all the results (Top-1 accuracy) of the MobileNetV2 network on the validation set was 98.5%. Compared with classic ResNetV2-50, Inception-V3, and backend-compressed Inception-V3, MobileNetV2 has a high accuracy, high recognition speed, and a small memory footprint. To further test the performance of the model, 250 images were used for each of the six categories of headland images as the test set for the experiments. The average of the harmonic mean of precision and recall (F1-score) of the MobileNetV2 network for all the categories of headland images reached 97%. The MobileNetV2 network exhibits good robustness and stability. The results of this study indicate that onboard computers on self-driving agricultural vehicles are able to employ the MobileNetV2 network for headland image recognition to meet the application requirements of headland environment sensing.

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