Nanodet-Ghost: a lightweight network for quality detection of wheat kernels appearance

Yining Zhang; Yunfei Wang; Yanan Wang; Zheng Wang; Rong Li; Zhixin Hua

doi:10.1117/12.2685080

1 August 2023 Nanodet-Ghost: a lightweight network for quality detection of wheat kernels appearance

Yining Zhang, Yunfei Wang, Yanan Wang, Zheng Wang, Rong Li, Zhixin Hua

Proceedings Volume 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023); 1275420 (2023) https://doi.org/10.1117/12.2685080
Event: 2023 3rd International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 2023, Hangzhou, China

Abstract

Wheat seed appearance quality detection is a preliminary step to obtain high-throughput phenotypic and wheat breeding information. However, the low portability due to parameter redundancy is a challenge for general detection methods based on deep learning. To address this issue, we designed a lightweight model (Nanodet-Ghost) for this task. The feature extraction structure of GhostNet optimized by ELU is applied to extract the features of wheat grains. Shallow and deep features are integrated to improve the discriminability of wheat grains. DIoU loss was used to improve detection accuracy and convergence speed. 6414 images of healthy, moldy, damaged and sprouted wheat grains were collected and divided into training set, validation set and test set in the ratio of 4:1:1. After 300 times of network training, the overall results showed that Nanodet-Ghost is suitable for quality detection of wheat grain appearance.

Citation Download Citation

Yining Zhang, Yunfei Wang, Yanan Wang, Zheng Wang, Rong Li, and Zhixin Hua "Nanodet-Ghost: a lightweight network for quality detection of wheat kernels appearance", Proc. SPIE 12754, Third International Conference on Computer Vision and Pattern Analysis (ICCPA 2023), 1275420 (1 August 2023); https://doi.org/10.1117/12.2685080

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KEYWORDS

Feature extraction

RGB color model

Feature fusion

Convolution

Performance modeling

Visual process modeling

Data modeling

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