Abstract
Eating healthily helps prevent disease, and it can be achieved by identifying the kinds and ingredients of the food to determine whether the diet is healthy. In this paper, we innovatively propose p-Faster R-CNN algorithm for healthy diet detection, which is based on Faster R-CNN with Zeiler and Fergus model (ZF-net) and Caffe framework. Before the input layer, the Gauss Pyramid is applied to form a multi-resolution pyramid of images, which expands the number and the scale of the samples. In the training stage, the multi-scale Spatial Pyramid Pooling Layer is added after the convolution layer to extract multi-scale features. To evaluate the performance of p-Faster R-CNN, we compare it with Fast R-CNN and Faster R-CNN. The experiment results demonstrated that p-Faster R-CNN increases the AP value of each kind of food by more than 2% compared with Faster R-CNN, and p-Faster R-CNN, Faster R-CNN are superior to Fast R-CNN in accuracy and speed. At last, the total dataset we established is used to construct the application of judging the healthy diet by uploading intake photos.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Melendez, S.: How machine learning will change what you eat. Mind and Machine (2016)
Siva, N.: Machine learning will keep us healthy. Lancet (2016)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 10, pp. 2–6 (2014)
He, K., Zhang, X.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2014)
Girshick, R.: Fast R-CNN. In: Computer Vision and Pattern Recognition, vol. 9, pp. 3–10 (2015)
Redmon, J., Divvala, S., Girshick, R.: You only look once: unified, real-time object detection, vol. 6, pp. 1–4 (2015)
Andelson, E.H., Anderson, C.H., Bergen, J.R., Burt, P.J., Ogden, J.M.: Pyramid methods in image processing. RCA Eng. 29(6), 33–41 (1984)
Ren, S., He, K.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 6, 1137–1149 (2015)
Lan, Z., Lin, M., Li, X.: Beyond Gaussian pyramid: multi-skip feature stacking for action recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 204–212 (2015)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Acknowledgements
This research is supported by The National Key Research and Development Program of China (2016YFC1300205).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Wan, Y., Liu, Y., Li, Y., Zhang, P. (2018). p-Faster R-CNN Algorithm for Food Detection. In: Romdhani, I., Shu, L., Takahiro, H., Zhou, Z., Gordon, T., Zeng, D. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 252. Springer, Cham. https://doi.org/10.1007/978-3-030-00916-8_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-00916-8_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00915-1
Online ISBN: 978-3-030-00916-8
eBook Packages: Computer ScienceComputer Science (R0)