Enhanced Mask R-CNN for Chinese Food Image Detection

Y. Li, X. Xu and C. Yuan

Mathematical Problems in Engineering, 2020, vol. 2020, 1-8

Abstract:

Food image detection plays an essential role in visual object detection, considering its applicability in solutions that improve people’s nutritional status and thus their health-care. At present, most food detection technologies are aimed at Western food and Japanese food, but few at Chinese foods. In this work, we exert effort to establish a Chinese food image dataset called CF-108 that can be used as an essential data basis for Chinese food image detection. The CF-108 dataset contains most Chinese dishes and covers large variations in presentations of the same category. In addition, we introduce a training architecture that replaces the traditional convolution in mask region convolutional neural network (Mask R-CNN) with depthwise separable convolution, namely, Mask R-DSCNN, to reduce the expensive computation cost. Experiments demonstrate that Mask R-DSCNN can significantly reduce resource consumption and improve Chinese food images’ detection efficiency without hurting too much accuracy.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:6253827

DOI: 10.1155/2020/6253827

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