Tajweed-YOLO: Object Detection Method for Tajweed by Applying HSV Color Model Augmentation on Mushaf Images
Tajweed is a basic knowledge of learning to read the Al-Qur’an correctly. Tajweed has many laws grouped into several parts so that only some people can memorize and implement Tajweed properly. Therefore, it is necessary to have an automatic detection system to facilitate the recognition of Tajweed, which can be used daily. This study presents Tajweed-YOLO, which applies the HSV color augmentation model to detect Tajweed objects in Mushaf images using YOLO. The contribution to this study was to compare the three versions of You Only Look Once (YOLO), i.e., YOLOv5, YOLOv6, and YOLOv7, and usage of the HSV color model augmentation to improve Tajweed detection performance. Comparing the three YOLO versions aims to solve problems in detecting small objects and recognizing various forms of Mushaf writing fonts in Tajweed detection. Meanwhile, the HSV color model aims to recognize Tajweed objects in various Mushaf and handle minority class problems. In this study, we collected four different Al-Qur’an mushaf with 10 Tajweed classes. The augmentation process can increase the detection performance by up to 85% compared to without augmentation 6th Class (Mad Jaiz Munfashil) using YOLOv6. The comparison of three YOLO versions concluded that YOLOv7 was better than YOLOv5 and YOLOv6, seen in data with augmentation and without augmentation. The evaluation results of mAP0.5 on 17 test data on the YOLOv7, YOLOv6, and YOLOv5 models are 80%, 69%, and 71%, respectively. These results prove that this research model’s results are suitable for the real-time detection of Tajweed.
V. Maarif, H. M. Nur, W. Rahayu, M. Informatika, and T. Informatika, “Aplikasi pembelajaran ilmu tajwid berbasis android,” J. Evolusi, vol. 6, no. 1, pp. 91–100, 2018.
M. A. Amir, Ilmu Tajwid Praktis. Pustaka Baitul Hikmah Harun Ar-Rasyid, 2019.
M. Lubis and A. R. Lubis, “Classification of Tajweed Al-Qur’an on Images Applied Varying Normalized Distance Formulas,” in Proceedings of the 3rd International Conference on Electronics, Communications and Control Engineering, 2020, pp. 21–25.
T. A. Zuraiyah, S. Madenda, R. A. Salim, and R. Noviana, “Tajweed Segmentation Using Pattern Recognition, Extraction and SURF descriptor Algorithms,” in IOP Conference Series: Materials Science and Engineering, 2020, vol. 846, no. 1, p. 12022.
R. Rizal, B. Bustami, and D. Azzahra, “Pendeteksi Tajwid Idgham Mutajanisain Pada Citra Al-Qur’an Menggunakan Fuzzy Associative Memory (FAM),” TECHSI-Jurnal Tek. Inform., vol. 11, no. 3, pp. 395–407, 2019.
A. Noeman and D. Handayani, “Detection of Mad Lazim Harfi Musyba Images Uses Convolutional Neural Network,” in IOP Conference Series: Materials Science and Engineering, 2020, vol. 771, no. 1, p. 12030.
G. H. Aly, M. Marey, S. A. El-Sayed, and M. F. Tolba, “YOLO Based Breast Masses Detection and Classification in Full-Field Digital Mammograms,” Comput. Methods Programs Biomed., vol. 200, p. 105823, 2021.
R. C. Joshi, M. K. Dutta, P. Sikora, and M. Kiac, “Efficient Convolutional Neural Network Based Optic Disc Analysis Using Digital Fundus Images,” in 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), 2020, pp. 533–536.
J. Chhatlani, T. Mahajan, R. Rijhwani, A. Bansode, and G. Bhatia, “DermaGenics-Early Detection of Melanoma using YOLOv5 Deep Convolutional Neural Networks,” in 2022 IEEE Delhi Section Conference (DELCON), 2022, pp. 1–6.
E. Prasetyo, N. Suciati, and C. Fatichah, “A comparison of yolo and mask r-cnn for segmenting head and tail of fish,” in 2020 4th International Conference on Informatics and Computational Sciences (ICICoS), 2020, pp. 1–6.
W. Chen, H. Huang, S. Peng, C. Zhou, and C. Zhang, “YOLO-face: a real-time face detector,” Vis. Comput., vol. 37, no. 4, pp. 805–813, 2021.
E. Tanuwijaya and C. Fatichah, “Penandaan Otomatis Tempat Parkir Menggunakan YOLO Untuk Mendeteksi Ketersediaan Tempat Parkir Mobil Pada Video CCTV,” Briliant J. Ris. dan Konseptual, vol. 5, no. 1, pp. 189–198, 2020.
T. Abuzairi, N. Widanti, A. Kusumaningrum, and Y. Rustina, “Implementasi Convolutional Neural Network Untuk Deteksi Nyeri Bayi Melalui Citra Wajah Dengan YOLO,” J. RESTI (Rekayasa Sist. Dan Teknol. Informasi), vol. 5, no. 4, pp. 624–630, 2021.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779–788.
J. Redmon and A. Farhadi, “YOLO9000: better, faster, stronger,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 7263–7271.
J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” arXiv Prepr. arXiv1804.02767, 2018.
A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “Yolov4: Optimal speed and accuracy of object detection,” arXiv Prepr. arXiv2004.10934, 2020.
C. Li et al., “YOLOv6: a single-stage object detection framework for industrial applications,” arXiv Prepr. arXiv2209.02976, 2022.
C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” arXiv Prepr. arXiv2207.02696, 2022.
N. P. Sutramiani, N. Suciati, and D. Siahaan, “MAT-AGCA: Multi Augmentation Technique on small dataset for Balinese character recognition using Convolutional Neural Network,” ICT Express, vol. 7, no. 4, pp. 521–529, 2021.
N. Song and Q. Du, “Classification of cervical lesion images based on CNN and transfer learning,” in 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC), 2019, pp. 316–319.
W. Wang, B. Wu, S. Yang, and Z. Wang, “Road damage detection and classification with Faster R-CNN,” in 2018 IEEE international conference on big data (Big data), 2018, pp. 5220–5223.
R. M. I. Rusyd, Panduan Praktis & Lengkap Tahsin, Tajwid, Tahfiz Untuk Pemula. Laksana, 2019.
N. Hassan, K. W. Ming, and C. K. Wah, “A Comparative Study on HSV-based and Deep Learning-based Object Detection Algorithms for Pedestrian Traffic Light Signal Recognition,” in 2020 3rd International Conference on Intelligent Autonomous Systems (ICoIAS), 2020, pp. 71–76.
V. Popov, M. Ostarek, and C. Tenison, “Practices and pitfalls in inferring neural representations,” Neuroimage, vol. 174, pp. 340–351, 2018.
W. Chen, W. Shen, L. Gao, and X. Li, “Hybrid Loss-Constrained Lightweight Convolutional Neural Networks for Cervical Cell Classification,” Sensors, vol. 22, no. 9, p. 3272, 2022.
Q. Al-Jubouri, R. J. Al-Azawi, M. Al-Taee, and I. Young, “Efficient individual identification of zebrafish using Hue/Saturation/Value color model,” Egypt. J. Aquat. Res., vol. 44, no. 4, pp. 271–277, 2018.
Z. Zou, Z. Shi, Y. Guo, and J. Ye, “Object detection in 20 years: A survey,” arXiv Prepr. arXiv1905.05055, 2019.
C. Guo, B. Fan, Q. Zhang, S. Xiang, and C. Pan, “Augfpn: Improving multi-scale feature learning for object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 12595–12604.
J. Du, “Understanding of object detection based on CNN family and YOLO,” in Journal of Physics: Conference Series, 2018, vol. 1004, no. 1, p. 12029.
Q. Xu, Z. Zhu, H. Ge, Z. Zhang, and X. Zang, “Effective Face Detector Based on YOLOv5 and Superresolution Reconstruction,” Comput. Math. Methods Med., vol. 2021, 2021.
J. Yu, Y. Jiang, Z. Wang, Z. Cao, and T. Huang, “Unitbox: An advanced object detection network,” in Proceedings of the 24th ACM international conference on Multimedia, 2016, pp. 516–520.
Copyright (c) 2023 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright in each article belongs to the author
- The author acknowledges that the RESTI Journal (System Engineering and Information Technology) is the first publisher to publish with a license Creative Commons Attribution 4.0 International License.
- Authors can enter writing separately, arrange the non-exclusive distribution of manuscripts that have been published in this journal into other versions (eg sent to the author's institutional repository, publication in a book, etc.), by acknowledging that the manuscript has been published for the first time in the RESTI (Rekayasa Sistem dan Teknologi Informasi) journal ;