Semantic Segmentation Algorithm of Rice Small Target Based on Deep Learning

Shuofeng Li, Bing Li (), Jin Li, Bin Liu and Xin Li
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Shuofeng Li: College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
Bing Li: College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
Jin Li: College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
Bin Liu: College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
Xin Li: Beijing Aerospace Automatic Control Institute, Beijing 100854, China

Agriculture, 2022, vol. 12, issue 8, 1-13

Abstract: At present, rice is generally in a state of dense adhesion and small granular volume during processing, resulting in no effective semantic segmentation method for rice to extract complete rice. Aiming at the above problems, this paper designs a small object semantic segmentation network model based on multi-view feature fusion. The overall structure of the network is divided into a multi-view feature extraction module, a super-resolution feature building module and a semantic segmentation module. The extraction ability of small target features is improved by super-resolution construction of small target detail features, and the learning ability of the network for small target features is enhanced and expanded through multi-view. At the same time, a dataset of quality inspection during rice processing was constructed. We train and test the model on this dataset. The results show that the average segmentation accuracy of the semantic segmentation model in this paper reaches 87.89%. Compared with the semantic segmentation models such as SegNet, CBAM, RefineNet, DeepLabv3+ and G-FRNet, it has obvious advantages in various indicators, which can provide rice quality detection and an efficient method of rice grain extraction.

Keywords: semantic segmentation; deep learning; small target; feature fusion module (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: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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