Enhancing the Effectiveness of the YOLO Model Through Caladium Leaf Images Generated by Generative Adversarial Networks
DOI:
https://doi.org/10.37385/jaets.v7i1.6624Keywords:
Caladium, GAN, Model, Object Detection, YOLOAbstract
The need for ornamental caladium plants is very popular, but there are several obstacles to recognizing its type. Caladium species classification using AI is needed to overcome the problem of misidentification among enthusiasts. This study uses the Generative Adversarial Network (GAN) algorithm to generate new images from the Caladium dataset: Amazon Caladium, Bicolor Caladium, White Queen Caladium, and Skull Caladium. We combine GAN with YOLOv5 to detect Caladium in real time to improve accuracy. The quality of the generated images is evaluated using the Kernel Inception Distance (KID) method, with the highest scores of 0.2320 for Amazon Caladium, 0.1966 for Bicolor, 0.1713 for Skull, and 0.1857 for White Queen, indicating close similarity to the original images. We chose the best model to generate three datasets: Original Dataset, Mixed Dataset (original images plus GAN-generated images), and Dataset consisting mainly of GAN images. The Mixed Dataset achieved the best results, with a mean Average Precision (mAP) of 0.695 for an Intersection over Union (IoU) of 0.50:0.95 outperforming the GAN dataset and the original Dataset. This training used 50 epochs, a learning rate of 0.0003, and a batch size of 16, to obtain the best model and significantly improve Caladium detection. From this experiment, it was found that the GAN, combined with the original data, was able to support the accuracy of YOLOv5 for real-time caladium classification and was also able to create new images that resembled the original leaves. In the mobile application, this model allows real-time identification of Caladium types, making it easier for users to buy Caladium according to the desired type.
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References
Abas, S. M., Abdulazeez, A. M., & Zeebaree, D. Q. (2022). A YOLO and convolutional neural network for the detection and classification of leukocytes in leukemia. Indonesian Journal of Electrical Engineering and Computer Science, 25(1), 200–213. https://doi.org/10.11591/ijeecs.v25.i1.pp200-213
Abusalim, S., Zakaria, N., Maqsood, A., Saboor, A., Yew, K. H., Mokhtar, N., & Abdulkadir, S. J. (2024). Multi-granularity tooth analysis via YOLO-based object detection models for effective tooth detection and classification. IAES International Journal of Artificial Intelligence, 13(2), 2081–2092. https://doi.org/10.11591/ijai.v13.i2.pp2081-2092
Amir, I., Suhaimi, H., Mohamad, R., Abdullah, E., & Pu, C. H. (2024). Hybrid encryption based on a generative adversarial network. Indonesian Journal of Electrical Engineering and Computer Science, 35(2), 971–978. https://doi.org/10.11591/ijeecs.v35.i2.pp971-978
Barkah, M., Dwitanto, F., & Utami, D. (2023). POLA PERILAKU PENGGEMAR TANAMAN HIAS DI MASA PANDEMI COVID-19.
Berrahal, M., & Azizi, M. (2022). Optimal text-to-image synthesis model for generating portrait images using generative adversarial network techniques. Indonesian Journal of Electrical Engineering and Computer Science, 25(2), 972–979. https://doi.org/10.11591/ijeecs.v25.i2.pp972-979
Betzalel, E., Penso, C., & Fetaya, E. (2024). Evaluation Metrics for Generative Models: An Empirical Study. Machine Learning and Knowledge Extraction, 6(3), 1531–1544. https://doi.org/10.3390/make6030073
Betzalel, E., Penso, C., Navon, A., & Fetaya, E. (2022). A Study on the Evaluation of Generative Models. http://arxiv.org/abs/2206.10935
Binkowski, M., Sutherland, D. J., Arbel, M., & Gretton, A. (2018). Demystifying MMD GANs. http://arxiv.org/abs/1801.01401
Chandra, R., Arifin Prasetyo, T., Lumbangaol, H. E., Siahaan, V., & Sianipar, J. I. (2024). Development of a Mobile-Based Application for Classifying Caladium Plants Using the CNN Algorithm. Jurnal Online Informatika, 9(1), 111–118. https://doi.org/10.15575/join.v9i1.1296
Cheng, B., Girshick, R., Dollár, P., Berg, A. C., & Kirillov, A. (2021). Boundary IoU: Improving Object-Centric Image Segmentation Evaluation. https://bowenc0221.github.io/boundary-iou
Cook, J., & Ramadas, V. (2020). When to consult precision-recall curves. Stata Journal, 20(1), 131–148. https://doi.org/10.1177/1536867X20909693
Darmawan, H., Yuliana, M., Zen, M., Hadi, S., & Sudibyo, W. (2024). Fine-tuning GAN Models with Unpaired Aerial Images for RGB to NDVI Translation in Vegetation Index Estimation. 14(5).
Du, J., Jiao, S., Shen, A., & Hu, Y. (2024). Optimizing YOLOv5s Object Detection through Knowledge Distillation algorithm. https://doi.org/https://doi.org/10.48550/arXiv.2410.12259
Garcia-Pajuelo, J., & Paiva-Peredo, E. (2024). Comparison and evaluation of YOLO models for vehicle detection on bicycle paths. IAES International Journal of Artificial Intelligence, 13(3), 3634–3643. https://doi.org/10.11591/ijai.v13.i3.pp3634-3643
Gresinta, E., & Risdiana, A. (2023). Identifikasi Penyakit pada Tanaman Keladi Hias (Caladium spp.) dengan Pemanfaatan Sistem Pakar. EduBiologia: Biological Science and Education Journal, 3(2), 131. https://doi.org/10.30998/edubiologia.v3i2.19328
Gunduz, M. S., & Isik, G. (2023). A new YOLO-based method for real-time crowd detection from video and performance analysis of YOLO models. Journal of Real-Time Image Processing, 20(1). https://doi.org/10.1007/s11554-023-01276-w
Jasim, R. M., & Atia, T. S. (2023). An evolutionary-convolutional neural network for fake image detection. Indonesian Journal of Electrical Engineering and Computer Science, 29(3), 1657–1667. https://doi.org/10.11591/ijeecs.v29.i3.pp1657-1667
Jimenez-Gaona, Y., Carrion-Figueroa, D., Lakshminarayanan, V., & Jose Rodriguez-Alvarez, M. (2024).
GAN-based data augmentation to improve breast ultrasound and mammography mass classification.
Biomedical Signal Processing and Control, 94. https://doi.org/10.1016/j.bspc.2024.106255
Jin, Y., Gao, H., Fan, X., Khan, H., & Chen, Y. (2022). Defect Identification of Adhesive Structure Based on DCGAN and YOLOv5. IEEE Access, 10, 79913–79924. https://doi.org/10.1109/ACCESS.2022.3193775
Kamiri, J., & Mariga, G. (2021). Research Methods in Machine Learning: A Content Analysis. In International Journal of Computer and Information Technology (Vol. 10, Issue 2). www.ijcit.com78
Kumari, N., Zhang, R., Shechtman, E., & Zhu, J.-Y. (2022). Ensembling Off-the-shelf Models for GAN Training. https://doi.org/https://doi.org/10.48550/arXiv.2112.09130
Li, Q., Li, Y., Duan, H., Kang, J., Zhang, J., Gan, X., & Xu, R. (2023). Improvement and Enhancement of YOLOv5 Small Target Recognition Based on Multi-module Optimization. https://doi.org/https://doi.org/10.48550/arXiv.2310.01806
Lim, S., & Kim, J. (2024). Advanced Object Tracking through Conditional Online Updates and Noise Suppression. 14(5).
Liu, Y., Sun, P., Wergeles, N., & Shang, Y. (2021). A survey and performance evaluation of deep learning methods for small object detection. Expert Systems with Applications, 172, 114602. https://doi.org/10.1016/J.ESWA.2021.114602
Mas, M. S., Saidah, S., & Ibrahim, N. (2024). Detection and counting of wheat ear using YOLOv8. International Journal of Electrical and Computer Engineering, 14(5), 5813–5823. https://doi.org/10.11591/ijece.v14i5.pp5813-5823
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