MASPP and MWASP: multi-head self-attention based modules for UNet network in melon spot segmentation

Sweet melon, and in particular, spotted melon, is one of the most profitable fruit crops for farmers in the international market. As the spot ratio impacts the melon’s visual appeal, it plays a significant role in shaping consumers’ initial impressions and influencing their decision to purchase a sp...

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Bibliographic Details
Published in:Journal of food measurement & characterization 2024-05, Vol.18 (5), p.3935-3949
Main Authors: Tran, Khoa-Dang, Ho, Trang-Thi, Huang, Yennun, Le, Nguyen Quoc Khanh, Tuan, Le Quoc, Ho, Van Lam
Format: Article
Language:English
Subjects:
Chemistry
Chemistry and Materials Science
Chemistry/Food Science
Consumers
Deep learning
Engineering
Food Science
Fruit crops
Fruits
Harvesting
Information technology
Melons
Methods
Modules
Original Paper
Semantics
Skin
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Summary:Sweet melon, and in particular, spotted melon, is one of the most profitable fruit crops for farmers in the international market. As the spot ratio impacts the melon’s visual appeal, it plays a significant role in shaping consumers’ initial impressions and influencing their decision to purchase a spotted melon. However, accurately determining the spot area on a melon’s skin is challenging due to the diverse sizes and colors of these spots among different types of melons. In this study, the novel networks based on UNet model have been proposed to accurately determine the spot area on melon skins after harvesting. First, Mask R-CNN model was employed to isolate the melons from unwanted objects and backgrounds. Then, the novel variants of the Atrous Spatial Pyramid Pooling (ASPP) and Waterfall Atrous Spatial Pooling (WASP) were developed based on the multi-head self-attention (MHSA) approach to efficiently enhance the original structures. Finally, the proposed modules were integrated into VGG16-UNet network to segment melons’ spots on its skin. The experimental results demonstrate that the proposed methods yielded promising outcomes, achieving a mean IoU of 89.86% and an accuracy of 99.45% across all classes. Moreover, it outperformed other existing models.
ISSN:2193-4126
2193-4134
DOI:10.1007/s11694-024-02466-1