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

Khoa Dang Tran, Trang Thi Ho, Yennun Huang, Nguyen Quoc Khanh Le, Le Quoc Tuan, Van Lam Ho

研究成果: 雜誌貢獻文章同行評審


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.
頁(從 - 到)3935-3949
期刊Journal of Food Measurement and Characterization
出版狀態接受/付印 - 2024

ASJC Scopus subject areas

  • 食品科學
  • 一般化學工程
  • 安全、風險、可靠性和品質
  • 工業與製造工程


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