Título: Application of machine learning models for tracking and counting heavy vehicles on highways
Autores: Brenner dos Santos Araujo, Acbal Rucas Andrade Achy, Daniel José Cerqueira Brito, Tiago Palma Pagano, Weiner Costa & Mario Sergio Souza de Almeida
Resumo: Accurate traffic volume estimation is essential for road infrastructure planning, particularly for the design and maintenance of asphalt pavements. This study presents a comparative evaluation of two object tracking algorithms, BoT-SORT and ByteTrack, applied to the task of monitoring heavy vehicles in real-world road environments using side-view videos. Detection was performed using both pre-trained and customized YOLOv8 models, with the latter tailored to the vehicle categories defined by the DNIT traffic manual. A single-line counting technique was employed to mitigate false negatives caused by occlusions. The experiments were conducted using real data from the BR-110 highway, and performance was evaluated based on precision, recall, and F1 score metrics. The results demonstrate that customized YOLOv8 models, particularly when combined with ByteTrack, achieved superior performance, with a significant reduction in false positives. Previous studies indicate that camera positioning and model adaptation to specific data are critical factors for enhancing detection accuracy in side-view scenarios, especially in environments subject to occlusions and heavy traffic. In this study, we observed that the use of customized models led to improved performance; however, no comparative experiments were conducted with different camera positions, which limits the generalizability of the findings.
Palavras-chave: YOLO; Tracking; CNN; ByteTrack; BoT-SORT; DNIT; Traffic; Camera.
Páginas: 8
Código DOI: 10.21528/CBIC2025-1184872
Artigo em PDF: CBIC_2025_paper1184872.pdf
Arquivo BibTeX:
CBIC_2025_1184872.bib