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- W4281861113 abstract "Achieving fast and accurate crack detection of winter jujubes is essential for guaranteeing jujube quality. However, intelligent crack inspection remains a challenging task due to diverse crack morphologies, low crack ratios and low contrast between cracks and jujubes. To address these issues, an intelligent algorithm for the crack damage assessment of winter jujubes was proposed which aimed to detect cracks and give a quantitative damage percentage. First, the GrabCut algorithm was used to obtain a jujube masks with accurate boundary information. Then, an attention feature fusion network based on the U-Net architecture (AFFU-Net) with a hybrid loss and residual refinement module (RRM) was proposed to achieve automated winter jujube crack detection. Finally, a damage percentage was calculated using the division calculation between the pixel area of the jujube and the crack mask. Experimental results demonstrated that our method outperformed the state-of-the-art methods in both detection efficiency and accuracy on our dataset. Moreover, our method was also effective and applicable for winter jujube crack detection in harsh environments that contain noise." @default.
- W4281861113 created "2022-06-13" @default.
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- W4281861113 date "2022-07-01" @default.
- W4281861113 modified "2023-10-05" @default.
- W4281861113 title "AFFU-Net: Attention feature fusion U-Net with hybrid loss for winter jujube crack detection" @default.
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- W4281861113 doi "https://doi.org/10.1016/j.compag.2022.107049" @default.
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