Tracking Ball Using YOLOv8 Method on Wheeled Soccer Robot with Omnidirectional Camera
DOI:
https://doi.org/10.12928/biste.v6i2.10816Keywords:
Deep Learning, YOLO, YOLOv8, Tracking Ball, OmnidirectionalAbstract
Object detection is very we often find in everyday life that facilitates every activities in the object recognition process, for example in the military field, intelligent transportation, face detection, robotics, and others. Detection target detection is one of the hotspots of research in the field of computer vision. The location and category of the target can be determined by using target detection. Currently, target detection has been applied in many fields, one of which includes image segmentation.You only look once (YOLO) is an algorithm that can perform object detection in realtime, YOLO itself always gets development and improvement from previous versions. YOLOv8 is a type of YOLO from the latest version. YOLOv8 is a new implementation of Deep Learning that connects the input (original image) with the output. This type of YOLOv8 algorithm uses A deep dive architecture, assisted by CNN and a new backbone which uses convolutional layers for pixels which when described will be shaped like a pyramid. YOLOv8 is a stable object detection processing method with 80% higher than the previous version of YOLO, which makes YOLOv8 a type of YOLO that is better at processing object data faster and more efficiently in Real-Time.The camera with omnidirectional system is able to detect spherical objects and other objects using the YOLOV8 model used. In performance testing with 320×320 and 416×416 frames, because it fits the grid structure of the YOLO architecture. YOLOv8 has a higher mAP value with a value of 95,5% compared to previous versions of YOLO. In the detection test, YOLOv8 has a better average object detection than the previous version of YOLO which is indicated by the number of objects detected more stable.
References
P. Adarsh, P. Rathi, and M. Kumar, “YOLO v3-Tiny: Object Detection and Recognition using one stage improved model,” 2020 6th Int. Conf. Adv. Comput. Commun. Syst. ICACCS 2020, pp. 687–694, 2020, https://doi.org/10.1109/ICACCS48705.2020.9074315.
T. Ahmad et al., “Object Detection through Modified YOLO Neural Network,” Sci. Program., vol. 2020, pp. 1–10, 2020, https://doi.org/10.1155/2020/8403262.
R. Bai, F. Shen, M. Wang, J. Lu, and Z. Zhang, “Improving Detection Capabilities of YOLOv8-n for Small Objects in Remote Sensing Imagery: Towards Better Precision with Simpliied Model Complexity Improving Detection Capabilities of YOLOv8-n for Small Objects in Remote Sensing Imagery: Towards Better Pre,” Res. Sq., pp. 0–9, 2023, https://doi.org/10.21203/rs.3.rs-3085871/v1.
T. Diwan, G. Anirudh, and J. V. Tembhurne, “Object detection using YOLO: challenges, architectural successors, datasets and applications,” Multimed. Tools Appl., vol. 82, no. 6, pp. 9243–9275, 2023, https://doi.org/10.1007/s11042-022-13644-y.
W. Fang, L. Wang, and P. Ren, “Tinier-YOLO: A Real-Time Object Detection Method for Constrained Environments,” IEEE Access, vol. 8, pp. 1935–1944, 2020, https://doi.org/10.1109/ACCESS.2019.2961959.
M. Hussain, “YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection,” Machines, vol. 11, no. 7, 2023, https://doi.org/10.3390/machines11070677.
P. Jiang, D. Ergu, F. Liu, Y. Cai, and B. Ma, “A Review of Yolo Algorithm Developments,” Procedia Comput. Sci., vol. 199, pp. 1066–1073, 2021, https://doi.org/10.1016/j.procs.2022.01.135.
M. Ju, H. Luo, Z. Wang, B. Hui, and Z. Chang, “The application of improved YOLO V3 in multi-scale target detection,” Appl. Sci., vol. 9, no. 18, 2019, https://doi.org/10.3390/app9183775.
S. Li, Y. Li, Y. Li, M. Li, and X. Xu, “YOLO-FIRI: Improved YOLOv5 for Infrared Image Object Detection,” IEEE Access, vol. 9, pp. 141861–141875, 2021, https://doi.org/10.1109/ACCESS.2021.3120870.
Y. Li, Q. Fan, H. Huang, Z. Han, and Q. Gu, “A Modified YOLOv8 Detection Network for UAV Aerial Image Recognition,” Drones, vol. 7, no. 5, 2023, https://doi.org/10.3390/drones7050304.
H. Lou et al., “DC-YOLOv8: Small-Size Object Detection Algorithm Based on Camera Sensor,” Electron., vol. 12, no. 10, pp. 1–14, 2023, https://doi.org/10.3390/electronics12102323.
M. Maity, S. Banerjee, and S. Sinha Chaudhuri, “Faster R-CNN and YOLO based Vehicle detection: A Survey,” Proc. - 5th Int. Conf. Comput. Methodol. Commun. ICCMC 2021, no. Iccmc, pp. 1442–1447, 2021, https://doi.org/10.1109/ICCMC51019.2021.9418274.
S. M. Nurin Miza Afiqah Andrie Dazlee, Syamimi Abdul Khalil, Shuzlina Abdul-Rahman, “Object Detection for Autonomous Vehicles with Sensor-based Technology Using YOLO,” IJISAE, pp. 129–134, 2022, https://doi.org/10.18201/ijisae.2022.276.
F. M. Talaat and H. ZainEldin, “An improved fire detection approach based on YOLO-v8 for smart cities,” Neural Comput. Appl., vol. 35, no. 28, pp. 20939–20954, 2023, https://doi.org/10.1007/s00521-023-08809-1.
Z. Trabelsi, F. Alnajjar, M. M. A. Parambil, M. Gochoo, and L. Ali, “Real-Time Attention Monitoring System for Classroom: A Deep Learning Approach for Student’s Behavior Recognition,” Big Data Cogn. Comput., vol. 7, no. 1, pp. 1–17, 2023, https://doi.org/10.3390/bdcc7010048.
R. Yang, W. Li, X. Shang, D. Zhu, and X. Man, “KPE-YOLOv5: An Improved Small Target Detection Algorithm Based on YOLOv5,” Electron., vol. 12, no. 4, pp. 1–13, 2023, https://doi.org/10.3390/electronics12040817.
X. Yue, H. Li, M. Shimizu, S. Kawamura, and L. Meng, “YOLO-GD: A Deep Learning-Based Object Detection Algorithm for Empty-Dish Recycling Robots,” Machines, vol. 10, no. 5, pp. 1–20, 2022, https://doi.org/10.3390/machines10050294.
Y. Zhang, Y. Sun, Z. Wang, and Y. Jiang, “YOLOv7-RAR for Urban Vehicle Detection,” Sensors, vol. 23, no. 4, 2023, https://doi.org/10.3390/s23041801.
L. L. Zhao and M. L. Zhu, “MS-YOLOv7:YOLOv7 Based on Multi-Scale for Object Detection on UAV Aerial Photography,” Drones, vol. 7, no. 3, 2023, https://doi.org/10.3390/drones7030188.
W. Xu, C. Cui, Y. Ji, X. Li, and S. Li, “YOLOv8-MPEB small target detection algorithm based on UAV images,” Heliyon, vol. 10, no. 8, p. e29501, 2024, https://doi.org/10.1016/j.heliyon.2024.e29501.
L. Shen, B. Lang, and Z. Song, “DS-YOLOv8-Based Object Detection Method for Remote Sensing Images,” IEEE Access, vol. 11, no. November, pp. 125122–125137, 2023, https://doi.org/10.1109/ACCESS.2023.3330844.
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