Pengenalan Jenis Masakan Melalui Gambar Mengunakan YOLO
Keywords:
program ruang, panti asuhan, sidoarjo, arsitektur perilaku, karakter ruang.Abstract
Most input systems in food calorie information applications still use manual input, where the user needs to write the name of the food. This can cause difficulties for the user who does not know the name of the food. But with the development of technology in object detection, this can be an easier and faster process by using images to recognize the type of food.
This research uses You Only Look Once method to recognize the type of food from the input images. The type of YOLO model used is the YOLOv3 model which has a modified convolution layer with Xception model that has high accuracy and detection speed.
The result obtained is that the modified YOLO model has higher accuracy and faster detection speed than the standard YOLOv3 model. The Highest mAP result achieved was 85.13% with 0.088742 seconds average detection time.
References
[1] Aguilar, E., Remeseiro, B., Bolaños, M., & Radeva, P. 2018. Grab, Pay, and Eat: Semantic Food Detection for Smart Restaurants. IEEE Transactions on Multimedia, 20(12), 3266–3275. https://doi.org/10.1109/TMM.2018.2831627
[2] Badan Pengembangan dan Pembinaan Bahasa. n.d. Hasil Pencarian - KBBI Daring. Retrieved April 5, 2021, from https://kbbi.kemdikbud.go.id/entri/masakan
[3] Brugiapaglia, A., & Destefanis, G. 2015. Effect of cooking method on the nutritional value of Piedmontese beef. August 2012, 6–10.
[4] Chollet, F. 2017. Xception: Deep learning with depthwise separable convolutions. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-Janua, 1800–1807. https://doi.org/10.1109/CVPR.2017.195
[5] CS231n Convolutional Neural Networks for Visual Recognition. n.d.. Retrieved April 5, 2021, from https://cs231n.github.io/convolutional-networks/
[6] Domínguez, R., Borrajo, P., & Lorenzo, J. M. 2015. The effect of cooking methods on nutritional value of foal meat.
Journal of Food Composition and Analysis, 43, 61–67. https://doi.org/10.1016/j.jfca.2015.04.007
[7] Hui, J. 2018. mAP (mean Average Precision) for Object Detection | by Jonathan Hui. Medium. https://jonathan-hui.medium.com/map-mean-average-precision-for-object-detection-45c121a31173
[8] Kawano, Y., & Yanai, K. 2015. Automatic Expansion of a Food Image Dataset Leveraging Existing Categories with Domain Adaptation. In L. Agapito, M. M. Bronstein, & C. Rother (Eds.), Computer Vision - ECCV 2014 Workshops (pp. 3–17). Springer International Publishing. https://doi.org/10.1007/978-3-319-16199-0_1
[9] Liu, C., Cao, Y., Luo, Y., Chen, G., Vokkarane, V., & Ma, Y. (2016). Deepfood: Deep learning-based food image recognition for computer-aided dietary assessment. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9677, 37–48. https://doi.org/10.1007/978-3-319-39601-9_4
[10] Mezgec, S., & Seljak, B. K. 2017. Nutrinet: A deep learning food and drink image recognition system for dietary assessment. Nutrients, 9(7), 1–19. https://doi.org/10.3390/nu9070657
[11] Pouladzadeh, P., & Shirmohammadi, S. 2017. Mobile multi-food recognition using deep learning. ACM Transactions on Multimedia Computing, Communications and Applications, 13(3s), 1–21. https://doi.org/10.1145/3063592
[12] Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. 2016. You only look once: Unified, real-time object detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-Decem, 779–788. https://doi.org/10.1109/CVPR.2016.91
[13] Redmon, J., & Farhadi, A. 2017. YOLO9000: Better, faster, stronger. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-Janua, 6517–6525. https://doi.org/10.1109/CVPR.2017.690
[14] Sahoo, D., Hao, W., Ke, S., Xiongwei, W., Le, H., Achananuparp, P., Lim, E. P., & Hoi, S. C. H. 2019. Food AI: Food image recognition via deep learning for smart food logging. ArXiv, May.
[15] Szegedy, C., Toshev, A., & Erhan, D. 2013. Deep Neural Networks for Object Detection. Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2, 2553–2561.
[16] Teng, J., Zhang, D., Lee, D. J., & Chou, Y. 2019. Recognition of Chinese food using convolutional neural network. Multimedia Tools and Applications, 78(9), 11155–11172. https://doi.org/10.1007/s11042-018-6695-9
[17] U.S. Department of Agriculture and U.S. Department of Health and Human Services. 2020. Dietary Guidelines for Americans 2020-2025. https://www.dietaryguidelines.gov/
[18] World Health Organization. 2003. Diet, Nutrition and the Prevention of Chronic Diseases.
[19] World Health Organization. 2020. Healthy diet. https://www.who.int/news-room/fact-sheets/detail/healthy-diet
