Enhanced Mask R-CNN for Chinese Food Image Detection

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.