AM-UNet: Field Ridge Segmentation of Paddy Field Images Based on an Improved MultiResUNet Network

Xulong Wu, Peng Fang, Xing Liu, Muhua Liu, Peichen Huang, Xianhao Duan, Dakang Huang and Zhaopeng Liu ()
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Xulong Wu: College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China
Peng Fang: College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China
Xing Liu: College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China
Muhua Liu: College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China
Peichen Huang: College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Xianhao Duan: College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China
Dakang Huang: College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China
Zhaopeng Liu: College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China

Agriculture, 2024, vol. 14, issue 4, 1-17

Abstract: In order to solve the problem of image boundary segmentation caused by the irregularity of paddy fields in southern China, a high-precision segmentation method based on the improved MultiResUNet model for paddy field mapping is proposed, combining the characteristics of paddy field scenes. We introduce the attention gate (AG) mechanism at the end of the encoder–decoder skip connections in the MultiResUNet model to generate the weights and highlight the response of the field ridge area, add an atrous spatial pyramid pooling (ASPP) module after the end of the encoder down-sampling, use an appropriate combination of expansion rates to improve the identification of small-scale edge details, use 1 × 1 convolution to improve the range of the sensory field after bilinear interpolation to increase the segmentation accuracy, and, thus, construct the AM-UNet paddy field ridge segmentation model. The experimental results show that the IoU, precision, and F1 value of the AM-UNet model are 88.74%, 93.45%, and 93.95%, respectively, and that inference time for a single image is 168ms, enabling accurate and real-time segmentation of field ridges in a complex paddy field environment. Thus, the AM-UNet model can provide technical support for the development of vision-based automatic navigation systems for agricultural machines.

Keywords: segmentation of ridges on fields; deep learning; attention gate mechanism; atrous spatial pyramid pooling (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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