ADVANCEMENTS IN SHAPE DETECTION: IMPLEMENTING YOLOV11 FOR ACCURATE CLASSIFICATION OF GEOMETRIC FORMS
DOI:
https://doi.org/10.63747/jeis.v1i2.20Keywords:
Confusion Matrix, Dataset, Google Colab, Precision, validationAbstract
The increasing complexity of shape detection and classification in various real-world applications presents significant challenges, particularly in environments characterized by clutter, occlusions, and varying lighting conditions. This study presents a comprehensive analysis of shape detection and classification using the YOLOv11 model, focusing on three geometric shapes: circles, squares, and triangles. A dataset of 4,630 images was meticulously collected and annotated, with 1,560 images for circles, 1,620 for squares, and 1,450 for triangles. The YOLOv11 model was trained over 50 epochs using a Tesla T4 GPU in Google Colab. Data augmentation techniques were applied to enhance model robustness. The average precision across all classes indicated a perfect model (100%) at 0.99 confidence. The overall mean average precision is 99.2% confirming the model's ability to detect the majority of relevant instances. Validation metrics mirrored training outcomes, with validation box loss decreasing from approximately 1.2 to 0.32, demonstrating strong generalization to unseen data. The F1-score averaged 0.95 across all classes at a confidence threshold of 0.862, highlighting an optimal balance between precision and recall. Confusion matrix analysis revealed 100% classification accuracy for circles and triangles, while squares were classified correctly 94% of the time, indicating minimal inter-shape misclassification. The findings underscore the potential of YOLOv11 for real-time shape detection applications in various domains, including manufacturing, robotics, and surveillance, paving the way for future integration of the model in real time setup.
References
Ahmed, S. F., Alam, M. S. B., Hassan, M., Rozbu, M. R., Ishtiak, T., Rafa, N., and Gandomi, A. H. (2023). Deep learning modelling techniques: current progress, applications, advantages, and challenges. Artificial Intelligence Review, 56(11), 13521-13617.
Alabyad, N., Hany, Z., Mostafa, A., Eldaby, R., Tagen, I. A., and Mehanna, A. (2024, March). From vision to precision: The dynamic transformation of object detection in autonomous systems. In 2024 6th International Conference on Computing and Informatics (ICCI) (pp. 332-344). IEEE.
Alhwaiti, Y., Khan, M., Asim, M., Siddiqi, M. H., Ishaq, M., and Alruwaili, M. (2025). Leveraging YOLO deep learning models to enhance plant disease identification. Scientific Reports, 15(1), 7969.
Ali, M. L., and Zhang, Z. (2024). The YOLO framework: A comprehensive review of evolution, applications, and benchmarks in object detection. Computers, 13(12), 336.
Dahiya, N., Prakash, D., Kundu, S., Kuttan, S. R., Suwalka, I., Ayadi, M., & Hashmi, A. (2025). Optimised RFO-tuned RF-DETR model for precision urine microscopy for renal and systemic disease diagnosis. Scientific Reports, 15(1), 25842.
Diwan, T., Anirudh, G., and Tembhurne, J. V. (2023). Object detection using YOLO: challenges, architectural successors, datasets and applications. multimedia Tools and Applications, 82(6), 9243-9275.
He, L. H., Zhou, Y. Z., Liu, L., Cao, W., and Ma, J. H. (2025). Research on object detection and recognition in remote sensing images based on YOLOv11. Scientific Reports, 15(1), 14032.
Kang, C. H., and Kim, S. Y. (2023). Real-time object detection and segmentation technology: an analysis of the YOLO algorithm. JMST Advances, 5(2), 69-76.
Le, H. F., Zhang, L. J., and Liu, Y. X. (2022). Surface defect detection of industrial parts based on YOLOv5. IEEE Access, 10, 130784-130794.
Li, L., Zhang, R., Xie, T., He, Y., Zhou, H., and Zhang, Y. (2024). Experimental design of steel surface defect detection based on MSFE-YOLO—An improved YOLOv5 algorithm with multi-scale feature extraction. Electronics, 13(18), 3783.
Liang, H., Wang, H., Mao, L., Liu, R., Wang, Z., & Wang, K. (2023). Bone Stick Image Classification Study Based on C3CA Attention Mechanism Enhanced Deep Cascade Network. IEEE Access, 11, 94057-94068.
Mao, M., and Hong, M. (2025). YOLO object detection for real-time fabric defect inspection in the textile industry: A review of YOLOv1 to YOLOv11. Sensors, 25(7), 2270.
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).
Sapkota, R., Flores-Calero, M., Qureshi, R., Badgujar, C., Nepal, U., Poulose, A. and Karkee, M. (2025). YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series. Artificial Intelligence Review, 58(9), 274.
Song, X., Xie, H., Gao, T., Cheng, N., and Gou, J. (2025). Improved YOLO-Based Pulmonary Nodule Detection with Spatial-SE Attention and an Aspect Ratio Penalty. Sensors, 25(14), 4245.
Trigka, M., & Dritsas, E. (2025). Edge and cloud computing in smart cities. Future Internet, 17(3), 118.
Upulie, H. D. I., and Kuganandamurthy, L. (2021). Real-time object detection using yolo: A review. Real-Time Object Detection using YOLO: A review, 196.
Verma, A., Kumar, V., & Yadav, R. K. (2024, March). Humerus bone fracture detection utilizing YOLOv4 algorithm: a deep learning approach. In 2024 2nd International Conference on Disruptive Technologies (ICDT) (pp. 1191-1196). IEEE.
Vijayakumar, A., and Vairavasundaram, S. (2024). YOLO-based object detection models: A review and its applications. Multimedia Tools and Applications, 83(35), 83535-83574.
Wang, C. Y., Yeh, I. H., & Mark Liao, H. Y. (2024, September). Yolov9: Learning what you want to learn using programmable gradient information. In European Conference on Computer Vision (pp. 1-21). Cham: Springer Nature Switzerland.
Xiao-Bo, L., Xiao, X., Ling, W., Zhi-Hua, C., Xin, G., & Ke-Xin, Z. (2023). Anchor-free based object detection methods and their application progress in complex scenes. Acta Automatica Sinica, 49(7), 1369-1392.
Yoon, J. H., Okwuosa, C. N., Aronwora, N. C., & Hur, J. W. (2025). Optimizing Defect Detection on Glossy and Curved Surfaces Using Deep Learning and Advanced Imaging Systems. Sensors, 25(8), 2449.
Zhang, Z., Zhang, Z., Li, G., and Xia, C. (2025). ZZ-YOLOv11: A Lightweight Vehicle Detection Model Based on Improved YOLOv11. Sensors, 25(11), 3399.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Journal of Engineering Innovation and Sustainability
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
In accordance with the Copyright Act of 1976, which became effective January 1, 1978, the following statement signed by each author must accompany the manuscript submitted: "I, the undersigned author, transfer all copyright ownership of the manuscript referenced above to the Journal of Engineering Innovation and Sustainability (JEIS), in the event the work is published. I warrant that the article is original, does not infringe upon any copyright or other proprietary right of any third party, is not under consideration by another journal, and has not been published previously. I have reviewed and approved the submitted version of the manuscript and agree to its publication in the Journal of Engineering Innovation and Sustainability." A copyright transfer form can be downloaded from the JEIS Website (https://jeis.com.ng/index.php/jeis). Author(s) will be consulted, whenever possible, regarding republication of material. All authors must have access to the data presented, and the authors and sponsor (if applicable) must agree to share original data with the editor if requested.
