Version 1
: Received: 15 May 2024 / Approved: 16 May 2024 / Online: 16 May 2024 (10:04:48 CEST)
How to cite:
Ortiz, J. P.; Valladolid, J. D.; Dutan, D.; Idrovo, P. A Novel Proposal for Traffic Officer Detection in Autonomous Vehicles using Convolutional Networks YOLO v3, v5, and v8. Preprints2024, 2024051078. https://doi.org/10.20944/preprints202405.1078.v1
Ortiz, J. P.; Valladolid, J. D.; Dutan, D.; Idrovo, P. A Novel Proposal for Traffic Officer Detection in Autonomous Vehicles using Convolutional Networks YOLO v3, v5, and v8. Preprints 2024, 2024051078. https://doi.org/10.20944/preprints202405.1078.v1
Ortiz, J. P.; Valladolid, J. D.; Dutan, D.; Idrovo, P. A Novel Proposal for Traffic Officer Detection in Autonomous Vehicles using Convolutional Networks YOLO v3, v5, and v8. Preprints2024, 2024051078. https://doi.org/10.20944/preprints202405.1078.v1
APA Style
Ortiz, J. P., Valladolid, J. D., Dutan, D., & Idrovo, P. (2024). A Novel Proposal for Traffic Officer Detection in Autonomous Vehicles using Convolutional Networks YOLO v3, v5, and v8. Preprints. https://doi.org/10.20944/preprints202405.1078.v1
Chicago/Turabian Style
Ortiz, J. P., Denys Dutan and Paul Idrovo. 2024 "A Novel Proposal for Traffic Officer Detection in Autonomous Vehicles using Convolutional Networks YOLO v3, v5, and v8" Preprints. https://doi.org/10.20944/preprints202405.1078.v1
Abstract
This article focuses on generating an alternative in order to identify traffic officers during driving. This research employed the latest You Only Look Once (YOLO) model, using a six-phase methodology: data collection, data preparation involving resizing and labeling, implementation of various filters to avoid overfitting, model training, prediction evaluation, and result interpretation. The YOLO model was applied across three iterations using a dataset of 1862 images. To enhance training efficiency and detection speed, the graphics processing unit (GPU) acceleration was utilized, further enhancing the experimental process. The results of this study revealed that the YOLOv8x variant produced the most promising results. This proposed model attained a remarkable F1 score of 0.95, bolstered by a confidence score of 0.631, with the potential for an increase to 0.80 in confidence without significantly compromising the F1-score. These findings are poised to make a substantial contribution to the broader research landscape, particularly in advancing the effectiveness of detection models for traffic officers.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
