This study investigates the optimization of tree detection in static images using YOLOv5, YOLOv8, and YOLOv11 models, leveraging a custom non-standard image bank created exclusively for this research. Objectives: To enhance tree detection in static images by comparing the performance of YOLOv5, YOLOv8, and YOLOv11 models. The comparison involved hyperparameter tuning and the application of various optimizers, aiming to improve model performance in terms of precision, recall, F1, and mean average precision (mAP). Design/Methodology/Approach: A custom image bank was utilized to train YOLOv5, YOLOv8, and YOLOv11 models. During training, the hyperparameters’ learning rate and momentum were tuned in combination with the optimizers SGD, Adam, and AdamW. Performance metrics, including precision, recall, F1, and mAP, were analyzed for each configuration. Key Results: The optimization process achieved precision values of 100% with Adam for YOLOv8 and SGD for YOLOv11, and recall of 91.5% with AdamW on YOLOv8. Additionally, mAP values reached 95.6% for AdamW on YOLOv8 and 95.2% for SGD on YOLOv11. Convergence times for mAP were also significantly reduced, demonstrating faster training and enhanced overall model performance. Originality/Research gap: This study addresses a gap in tree detection using YOLO models trained on non-standard image banks, a topic that is less commonly explored in the literature. The exclusive development of a custom image bank further adds novelty to the research. Practical Implications: The findings underscore the effectiveness of model optimization in tree detection tasks using custom datasets. This methodology could be extended to other applications requiring object detection in non-standard image banks. Limitations of the investigation: This study is limited to tree detection within a single custom dataset and does not evaluate the generalizability of these optimizations to other datasets or object detection tasks.

Resumo— Este trabalho propõe uma revisão sistemática sobre o uso de Redes Neurais Artificiais (RNA), mais especificamente, Redes Neurais Convolucionais e Visão Computacional para a identificação precoce do câncer de mama, extraindo informações em quatro importantes bases de dados: ACM, IEEE, MEDLINE e SCOPUS. A partir de pesquisas realizadas nestas quatro bases foram elencados mais de 98 artigos relacionados aos tópicos deste trabalho. Neste artigo de TCC, será apresentados resultados destas pesquisas, fundamentação teórica, metodologia de pesquisa, análises de dados e conclusão. É esperado que a pesquisa direcione novos avanços nas áreas das ciências médicas e ciências da computação, além de conceituar e apresentar informações que possibilitem a imersão de novos pesquisadores. Serão apresentados os principais algoritmos, bases de dados e sugestões para avaliação de desempenho das técnicas.