Automatic Cigarette Object Concealment in Video using R-CNN
Cigarettes are packaged processed tobacco products, produced from the Nicotiana Tabacum, Nicotiana Rustica plants and other species or synthetics that contain nicotine with or without additives. Smoking is known to the public as one of the causes of death in the world that is quite large such as asthma, lung infections, oral cancer, throat cancer, lung cancer, heart attacks, strokes, dementia, erectile dysfunction (impotence), and so on. This research aims to build an application that can recognize cigarettes automatically and conceal pictures so that people especially minors are not affected by cigarettes. The application is built using the Region-based Convolutional Neural Network (R-CNN) method. The study uses images that have cigarette objects in them. The test is carried out to find out the application performance such as the level of application accuracy in recognizing cigarette objects. Based on the test results with a sample of 126 cigarette images, the application built is able to recognize cigarette objects by obtaining an accuracy value of 63%.
 P. A. Wicaksana, I. M. Sudarma, and D. C. Khrisne, “PENGENALAN POLA MOTIF KAIN TENUN GRINGSING MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK DENGAN MODEL ARSITEKTUR ALEXNET,” J. SPEKTRUM, vol. 6, no. 3, pp. 159–168, 2019.
 I. M. Wismadi, D. C. Khrisne, and I. M. A. Suyadnya, “Detecting the ripeness of harvest-ready dragon fruit using smaller VGGNet-like network,” J. Electr. Electron. Informatics, vol. 3, no. 2, pp. 35–38, 2020.
 D. C. Khrisne and T. Hendrawati, “Indonesian Alphabet Speech Recognition for Early Literacy using Convolutional Neural Network Approach,” J. Electr. Electron. Informatics, vol. 4, no. 1, pp. 34–37.
 K. H. Indrani, D. C. Khrisne, and I. M. A. Suyadnya, “Android Based Application for Rhizome Medicinal Plant Recognition Using SqueezeNet,” J. Electr. Electron. Informatics, vol. 4, no. 1, pp. 10–14.
 H. Filipe De Sousa Russa, “Computer Vision: Object recognition with deep learning applied to fashion items detection in images Master in Data Analysis,” no. September, 2017.
 O. Hamalik, Media Pendidikan. 1986.
 L. Deng, D. Yu, and B. — Delft, “Deep Learning: Methods and Applications Foundations and Trends R in Signal Processing,” Signal Processing, vol. 7, pp. 3–4, 2013, doi: 10.1561/2000000039.
 K. P. Danukusumo, “Implementasi Deep Learning Menggunakan Convolutional Neural Network Untuk Klasifikasi Citra Candi Berbasis GPU”.,” 2017.
 M. Zufar and B. Setiyono, “Convolutional Neural Networks Untuk Pengenalan Wajah Secara Real-time,” J. Sains dan Seni ITS, vol. 5, no. 2, p. 128862, 2016.
 R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 580–587, 2014, doi: 10.1109/CVPR.2014.81.
 S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1137–1149, 2017, doi: 10.1109/TPAMI.2016.2577031.
 K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask R-CNN,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 2, pp. 386–397, 2020, doi: 10.1109/TPAMI.2018.2844175.
 D. N. Rizky, “Deteksi Tanda Nomor Kendaraan Bermotor Pada Media Streaming Dengan Algoritma Convolutional Neural Network Menggunakan Tensorflow,” no. March, 2018.
 Y. Yue, T. Finley, F. Radlinski, and T. Joachims, “A support vector method for optimizing average precision,” Proc. 30th Annu. Int. ACM SIGIR Conf. Res. Dev. Inf. Retrieval, SIGIR’07, pp. 271–278, 2007, doi: 10.1145/1277741.1277790.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.