This paper presents a comprehensive evaluation of various YOLO architectures for smoke and wildfire detection, including YOLOv5, YOLOv6, YOLOv7, YOLOv8, and YOLO-NAS. The study aims to assess their effectiveness in early detection of wildfires using the Foggia dataset, comprising 8,974 images specifically designed for this purpose. Performance evaluation employs metrics such as Recall, Precision, F1-score, and mean Average Precision to provide a holistic assessment of the models’ performance. The study follows a rigorous methodology involving fixed epochs, continuous performance tracking, and unbiased testing. Results show that YOLOv5, YOLOv7, and YOLOv8 exhibit a balanced performance across all metrics in both validation and testing. YOLOv6 performs slightly lower in recall during validation but achieves a good balance on testing. YOLO-NAS variants excel in recall, making them suitable for critical applications. However, precision performance is lower for YOLO-NAS models. Visual results demonstrate that the top-performing models accurately identify most instances in the test set. However, they struggle with distant scenes and poor lighting conditions, occasionally detecting false positives. In favorable conditions, the models perform well in identifying relevant instances. We conclude that no single model excels in all aspects of smoke and wildfire detection. The choice of model depends on specific application requirements, considering accuracy, recall, and inference time. This research contributes to the field of computer vision in smoke and wildfire detection, providing a foundation for improving detection systems and mitigating the impact of wildfires. Researchers can build upon these findings to propose modifications and enhance the effectiveness of wildfire detection systems.

This review marks the tenth anniversary of You Only Look Once (YOLO), one of the most influential frameworks in real-time object detection. Over the past decade, YOLO has evolved from a streamlined detector into a diverse family of architectures characterized by efficient design, modular scalability, and cross-domain adaptability. The paper presents a technical overview of the main versions (from YOLOv1 to YOLOv13), highlights key architectural trends, and surveys the principal application areas in which YOLO has been adopted. It also addresses evaluation practices, ethical considerations, and potential future directions for the framework’s continued development. The analysis aims to provide a comprehensive and critical perspective on YOLO’s trajectory and ongoing transformation.

The fashion industry often falls short of sustainability goals, but contemporary technological advancements offer a wide range of tools to address this issue. Artificial Intelligence (AI) has emerged as a particularly promising ally in promoting sustainability in fashion. This literature review explores how AI can contribute to the fashion industry’s sustainability, highlighting its potential benefits and limitations. Following PRISMA guidelines, we conducted a review of scientific documents, focusing on the period from 2010 to 2022. After a meticulous selection process, we analyzed 37 scholarly articles to distill their key insights and contributions. Our findings demonstrate that AI has diverse applications in different aspects of the fashion industry, enhancing sustainability efforts in supply chain management, creative design, sales and promotion, waste control, and data analysis. While AI offers significant potential, it is important to acknowledge limitations, such as the volume of data required and associated implementation costs. The reviewed literature aligns with the multifaceted nature of sustainability, emphasizing responsible resource management, accessible services, and efficient customer satisfaction, both now and in the future. In conclusion, despite some reservations, AI stands as a crucial partner in guiding the fashion industry toward a more sustainable future.

Land cover classification (LCC) is a process used to categorize the Earth's surface into distinct land types. This classification is vital for environmental conservation, urban planning, agricultural management, and climate change research, providing essential data for sustainable decision-making. The use of multispectral imaging (MSI), which captures data beyond the visible spectrum, has emerged as one of the most utilized image modalities for addressing this task. Additionally, semantic segmentation techniques play a vital role in this domain, enabling the precise delineation and labeling of land cover classes within imagery. The integration of these three concepts has given rise to an intriguing and ever-evolving research field, witnessing continuous advancements aimed at enhancing multispectral semantic segmentation (MSSS) methods for LCC. Given the dynamic nature of this field, there is a need for a thorough examination of the latest trends and advancements to understand its evolving landscape. Therefore, this paper presents a review of current aspects in the field of MSSS for LCC, addressing the following key points: (1) prevalent datasets and data acquisition methods, (2) preprocessing methods for managing MSI data, (3) typical metrics and evaluation criteria used for assessing performance of methods, (4) current techniques and methodologies employed, and (5) spectral bands beyond the visible spectrum commonly utilized. Through this analysis, our objective is to provide valuable insights into the current state of MSSS for LCC, contributing to the ongoing development and understanding of this dynamic field while also providing perspectives for future research directions.

This paper provides a thorough analysis and comparison of the YOLOv5 and YOLOv8 models for wildfire and smoke detection, using the Foggia dataset for evaluation. The study examines the small (s), medium (m), and large (l) variants of each architecture and employs various metrics, including recall, precision, F1-Score, and mAP@50, to assess performance. Additional considerations such as training and inference times, along with the number of epochs required for optimal recall, are also evaluated to gauge the models’ real-world efficiency and effectiveness. Quantitatively, YOLOv5 models generally outperform YOLOv8, with the YOLOv5s variant achieving the highest scores across all metrics. However, visual assessments reveal that YOLOv8 models exhibit similar, and in some cases superior, capabilities, particularly in detecting dark and dense smoke. Training times favor YOLOv5 models, contributing to their efficiency, and their shorter inference times offer advantages for real-time applications. While the “best model” variants confirm YOLOv5’s numerical dominance, YOLOv8’s “best models” also display competitive performance. Future research will explore model evaluation on diverse datasets and hyperparameter optimization to further enhance performance, adaptability, and applicability in various real-world object detection scenarios.