Object Detection with a Webcam Using the Python Programming Language
DOI:
https://doi.org/10.37385/jaets.v2i2.247Keywords:
Webcam, Python, Image Processing, AutomationAbstract
Technology developed rapidly along the times, various ways are done to make works easier. One of them is by utilizing artificial intelligence, likes the use of a webcam as a sensor in detecting an object through several stages of image processing. There was research on object recognition previously using three measurement parameters based on color, shape, and size of objects. It is used a webcam as the sensing sensor, and image processing is processed with python programming. In this article, writer described about image processing for the detection of objects used in the research. The results of this device have been tested and are able to detect objects properly based on predetermined color, shape and size. Object detection using a webcam can work properly according to what the author wants.
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References
Aditya, M. R. V., Husni, N. L., Pratama, D. A., & Handayani, A. S. (2020). Penerapan Sistem Pengolahan Citra Digital Pendeteksi Warna pada Starbot. TEKNIKA, 14(2), 185-191.
Andrizal, Hidayat, A., Susanti, R. R., Chadry. (2016) .Computare Vision Berbasis Camera dan Mini PC untuk Identifikasi Kecacatan Penutup Kemasan Minuman Kaleng. Jurnal Ilmiah Poli Rekayasa, 12(1).
Alfaro-Almagro, F., Jenkinson, M., Bangerter, N. K., Andersson, J. L., Griffanti, L., Douaud, G., ... & Smith, S. M. (2018). Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage, 166, 400-424.
Aviolita, E. (2019). Pendeteksi Penyakit Tanaman Buah Naga Menggunakan Kamera Berbasis Raspberry Pi (Doctoral dissertation, Institut Teknologi Nasional Malang).
Bradski, G., & Kaehler, A. (2008). Learning OpenCV: Computer vision with the OpenCV library. " O'Reilly Media, Inc.".
Daud, N. B. (2007). Application of colors sensor in an automated system. Technical University Malaysia.
Ghanie, C. E. S., & Setiawan, F. B. (2020). Penerapan Sistem Pan-Tilt Camera untuk Deteksi Objek berdasarkan Warna menggunakan Raspberry Pi. In Seminar Nasional Teknik Elektro 5(1), 92-96.
Handoyo, E. D. (2006). Perancangan mini image editor versi 1.0 sebagai aplikasi penunjang mata kuliah digital image processing. Jurnal Teknik Informatika dan Sistem Informasi, 2(2), 219230.
Januar Insani, O. (2016). Implementasi Lane Detection Dengan Metode Hough Transform Untuk Penilain mengemudi Berdasarkan Marka Jalan (Studi Kasus Sukses Mandiri) (Doctoral dissertation, Universitas Komputer Indonesia).
Juliando, J., & Husin, Z. (2019). Rancang Bangun Alat Penyortir Minuman Kaleng Menggunakan Camera Dan Deteksi Warna (Doctoral dissertation, Sriwijaya University).
Mubarrok, A. R., & Rahmawati, D. (2020). Rancang Bangun Timbangan Buah Anggur Digital Otomatis Berbasis Webcam Menggunakan Transformasi Hough. Science Electro, 12(2).
Najmurrokhman, A., Nugraha, A., Kusnandar, U. K., & Wibowo, B. (2017). Perancangan dan Realisasi Sistem Pendeteksi Objek Menggunakan Perangkat Lunak. in Prosiding Seminar Nasional Ilmu Pengetahuan dan Teknologi. Cimahi: Jenderal Achmad Yani (SNIJA)2017.
Prayitno, Y. P., & Harianto, M. C. W. (2011). Rancang Bangun Aplikasi Pendeteksi Bentuk dan Warna Benda Pada Mobile Robot Berbasis Webcam.
Pulungan, A. B., & Nafis, Z. (2021). Rancangan Alat Pendeteksi Benda dengan Berdasarkan Warna, Bentuk, dan Ukuran dengan Webcam. JTEIN: Jurnal Teknik Elektro Indonesia, 2(1), 49-54.
Purwanti, S., Febriani, A., Mardeni, M., & Irawan, Y. (2021). Temperature Monitoring System for Egg Incubators Using Raspberry Pi3 Based on Internet of Things (IoT). Journal of Robotics and Control (JRC), 2(5), 349-352.
Syafriadi, M. (2017). Rancang Bangun Alat Pendeteksi Warna Menggunakan Kamera Dan Output Suara Berbasis Komputer (Doctoral Dissertation, Politeknik Negeri Sriwijaya).
Yulian, F., & Mayasari, Z. M. Pengembangan Teknik Pengolahan Dan Analisis Citra Penginderaan Jauh Melalui Perancangan Tapis Morfologi Matematik.
Zarwani, H. (2019). Rancang Bangun Alat Penyortir Buah Tomat Berdasarkan Ukuran Dan Warna Menggunakan Metode Segmentasi Hsv Berbasis Raspberry Pi 3b+.