Real-Time Chinese Chess Piece Character Recognition using Edge AI
DOI:
https://doi.org/10.30871/jaic.v9i4.10056Keywords:
Computer Vision, Deep Learning, Deep Learning (DL), Jetson NanoAbstract
This research focuses on developing a character analysis system on Chinese chess pieces (xiangqi) using computer vision technology with the deep learning framework PyTorch. The system is designed to detect and interpret text written on chess pieces in real time, making it easier for players to identify the function of each piece. The implementation is done using a web camera and can be applied to embedded devices such as Jetson Nano. This research aims to develop an automatic recognition system that can help players better understand the game of xiangqi by identifying characters on pieces in real time. The test results show that the system successfully recognized 14 pieces correctly. The system developed using Jetson Nano can directly process image data with a processing time of 0.0222 seconds. This data is obtained from the average of each FPS image from the web camera.
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[1] T. Kasa, R. Adha, and S. Haraha, “Fungsi Xiangqi Bagi Masyarakat Tionghoa Di Kota Medan,” Jurnal Cakrawala Mandarin Asosiasi Program Studi Mandairn Indonesia, vol. 1, no. 2, pp. 49–56, 2017.
[2] A. lestari purba, “Designing a Chess Game Application Using the Android-Based Breadth First Search Method,” Journal Of Computer Science And Informatics Engineering (CoSIE), vol. 03, no. 3, pp. 123–130, 2024, [Online]. Available: http://creativecommons.org/licenses/by-sa/4.0/
[3] M. H. Kurniawan and D. Udjulawa, “Perbandingan Performa Algoritma Minimax Dan Alpha-Beta Pruning Pada Game Catur Cina,” 2020.
[4] C. N. Sari, M. Istoningtyas, and M. Rosario, “Jurnal Politeknik Caltex Riau,” 2019. [Online]. Available: https://jurnal.pcr.ac.id/index.php/jkt/
[5] A. Purno and W. Wibowo, “Implementasi Teknik Computer Vision Dengan Metode Colored Markers Trajectory Secara Real Time,” 2016.
[6] J. Subur et al., “Pemanfaatan Teknologi Computer Vision untuk Deteksi Ukuran Ikan Bandeng dalam Membantu Proses Sortir Ikan,” vol. 7, Jan. 2024.
[7] R. S. Wijaya, W. Saputra, S. Prayoga, and E. R. Jamzuri, “Application of Object Detection and Face Recognition with Customize Dataset on Service ROobot,” in 7th International Conference on Applied Engineering (ICAE 2024), Dec. 2024, pp. 51-65.
[8] R. S. Wijaya, R. A. Fatekha, S. Prayoga, D. Andrawan, N. Nazhifah, and M. A. B. Nugroho, “Penerapan Visual Servoing Robot Lengan dengan Metode Color Recognition sebagai Pemindah Objek Dua Warna Berbeda,” Journal of Applied Electrical Engineering, vol. 9, no. 1, pp. 51-57, 2025.
[9] S. Lu, “Deep learning for object detection in video,” in Journal of Physics: Conference Series, Institute of Physics Publishing, Mar. 2019. doi: 10.1088/1742-6596/1176/4/042080.
[10] S. Ilahiyah and A. Nilogiri, “Implementasi Deep Learning Pada Identifikasi Jenis Tumbuhan Berdasarkan Citra Daun Menggunakan Convolutional Neural Network,” JUSTINDO (Jurnal Sistem & Teknologi Informasi Indonesia), vol. 3, 2018.
[11] M. Haris, T. Pustaka, M. H. Diponegoro, S. Kusumawardani, and I. Hidayah, “Tinjauan Pustaka Sistematis: Implementasi Metode Deep Learning pada Prediksi Kinerja Murid (Implementation of Deep Learning Methods in Predicting Student Performance: A Systematic Literature Review),” 2021.
[12] A. Santoso and G. Ariyanto, “Implementasi Deep Learning Berbasis Keras Untuk Pengenalan Wajah,” Jurnal Teknik Elektro, vol. 18, no. 01, 2018, [Online]. Available: https://www.mathworks.com/discovery/convol
[13] I. Virgiawan, F. Maulana, M. A. Putra, D. D. Kurnia, and E. Sinduningrum, “Deteksi dan tracking objek secara real-time berbasis computer vision menggunakan metode YOLO V3,” Jurnal Ilmiah Multidisiplin Indonesia, vol. 3, no. 3, 2024, [Online]. Available: https://journal.ikopin.ac.id/index.php/humantech
[14] R. S. Wijaya, S. Hasibuan, A. Wibisana, E. Rudiawan, and M. A. B. N. Jamzuri, “Comparative Study of Deep Learning Algorithms Between YOLOv5 and Mobilenet-SSDv2 As Fast and Robust Outdoor Object Detection Solutions,” in Proceedings of the 7th International Conference on Applied Engineering (ICAE 2024), Dec. 2024, p. 94.
[15] C.-Y. Fu, W. Liu, A. Ranga, A. Tyagi, and A. C. Berg, “DSSD : Deconvolutional Single Shot Detector,” Jan. 2017,[Online].Available:http://arxiv.org/abs/1701.06659
[16] W. Liu et al., “SSD: Single Shot MultiBox Detector,” Dec. 2015, doi: 10.1007/978-3-319-46448-0_2.
[17] K. R. D. B. A. A. E. J.-Y. Chen Zhihao, Real Time Object Detection, Tracking, and Distance and Motion Estimation based on Deep Learning: Application to Smart Mobility. IEEE, 2019.
[18] S. A. Sanchez, H. J. Romero, and A. D. Morales, “A review: Comparison of performance metrics of pretrained models for object detection using the TensorFlow framework,” in IOP Conference Series: Materials Science and Engineering, Institute of Physics Publishing, Jun. 2020. doi: 10.1088/1757-899X/844/1/012024.
[19] Sukusvieri Andrianto, “Implementasi Metode Single Shot Detector (SSD),” 2020.
[20] R. S. Wijaya, S. Santonius, A. Wibisana, E. R. Jamzuri, and M. A. B. Nugroho, “Comparative Study of YOLOv5, YOLOv7, and YOLOv8 for Robust Outdoor Detection,” Journal of Applied Electrical Engineering, vol. 8, no. 1, pp. 37-43, 2024.
[21] S. Zhang, L. Wen, X. Bian, Z. Lei, and S. Z. Li, “Single-Shot Refinement Neural Network for Object Detection,” Dec. 2018.
[22] Hidayatulloh Syarif Muhammad, “Sistem Pengenalan Wajah Menggunakan Metode Yolo (You Only Look Once),” 2021.
[23] Arganata Rifai Ahmad, “Analisis Perhitungan Bibit Ikan Gurame Menggunakan Webcam Dengan Metode Yolo (You Only Look Once),” 2020.
[24] A. C. S. S. A. Supriyanto Hadi, “Deteksi Helm Keselamatan Menggunakan Jetson Nano dan YOLOv7,” Journal Of Applied Computer Science And Technology (JACOST), vol. 5, 2024, doi: 10.52158/jacost.637.
[25] N. L. A. F. Ilman Zidni Mukhamad, “Perbandingan Performa Jetson Nano, Jetson Xavier NX dan Lenovo Legion 5 terhadap Penggunaan YOLOv7,” Apr. 2024.
[26] M. Luqman Bukhori and E. E. Prasetiyo, “Sistem Deteksi Masker Berbasis Jetson Nano dengan Deep Learning Framework TensorFlow,” Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, Sep. 2022.
[27] M. M. H. T. H. W. W. J. M. G. I. Aryeni Illa, “Application of Computer Vision for Real-Time Detection of Fruit Color and Size in Fruit Sorter,” Journal Of Applied Electrical Engineering, vol. 7, pp. 1–6, Dec. 2023.
[28] Prisunia Fitriyati Sofianisa, “Pemanfaatan Jetson Nano Nvidia Untuk Mendeteksi Penggunaan Masker Secara Real-Time Menggunakan Opencv Python,” 2023.
[29] A. Faqih, K. Mutmainnah, and M. R. Afifah, “Seperation Deteksi Kendaraan Pada Citra Digital Dengan Menggunakan Algoritma YOLO (You Only Look Once),” Jurnal Teknik Informatika dan Elektro, vol. 3, no. 2, 2021, [Online]. Available: https://jurnal.ugp.ac.id
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Copyright (c) 2025 Ryan Satria Wijaya, Atika Yunisa Anadia, Rifqi Amalya Fatekha, Senanjung Prayoga
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