Rice grains and grain impurity segmentation method based on a deep learning algorithm-NAM-EfficientNetv2

Liu, Qinghua; Liu, Weikang; Zhe, Tiantian; Ding, Bochuan; Liang, Zhenwei

doi:10.1016/j.compag.2023.107824

Cited by 6 publications

(1 citation statement)

References 11 publications

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“…In object detection algorithms, the purpose of the attention mechanism is to apply more weight to the information to help solve a problem in a specific scenario by ignoring the irrelevant information and focusing on the key information, thereby improving detection performance. In this study, we chose four attention mechanisms, the CBAM [20], CA module [21], ECA module [22], and NAM [23], with which to conduct experiments.…”

Section: The Attention Mechanism Modulementioning

confidence: 99%

Quality Detection and Grading of Rose Tea Based on a Lightweight Model

Ding,

Chen,

Gui

et al. 2024

Foods

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Rose tea is a type of flower tea in China’s reprocessed tea category, which is divided into seven grades, including super flower, primary flower, flower bud, flower heart, yellow flower, scattered flower, and waste flower. Grading rose tea into distinct quality levels is a practice that is essential to boosting their competitive advantage. Manual grading is inefficient. We provide a lightweight model to advance rose tea grading automation. Firstly, four kinds of attention mechanisms were introduced into the backbone and compared. According to the experimental results, the Convolutional Block Attention Module (CBAM) was chosen in the end due to its ultimate capacity to enhance the overall detection performance of the model. Second, the lightweight module C2fGhost was utilized to change the original C2f module in the neck to lighten the network while maintaining detection performance. Finally, we used the SIoU loss in place of the CIoU loss to improve the boundary regression performance of the model. The results showed that the mAP, precision (P), recall (R), FPS, GFLOPs, and Params values of the proposed model were 86.16%, 89.77%, 83.01%, 166.58, 7.978, and 2.746 M, respectively. Compared with the original model, the mAP, P, and R values increased by 0.67%, 0.73%, and 0.64%, the GFLOPs and Params decreased by 0.88 and 0.411 M, respectively, and the speed was comparable. The model proposed in this study also performed better than other advanced detection models. It provides theoretical research and technical support for the intelligent grading of roses.

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