## 6 Conclusions  
The objective of this work is to propose a novel methodology to detect motorcycle riders and identify helmet violations. As helmet usage is highly associated with lower  
Figure 6: F1 and precision-recall curves for the trained YOLOv8 model (**left**: YOLOv8, **right**: YOLOv8 + DCGANs + TTA). Figure 7: Inference of trained DCGANs + YOLOv8 + TTA model on test data. crash risk, the methodology proposed in this research aims to improve safety overall by enforcing traffic laws. This study uses a CNN-based solution for helmet detection and expands the CNN training set using synthetic data, which were generated using DCGANs. This procedure was conducted to improve both identification and classification outcomes. The latest YOLO algorithm to date (i.e., YOLOv8) and test time augmentation were also used to improve the model performance. Findings reveal that combining original and synthetic images improves the ability to detect helmets. More specifically, by generating synthetic images for the most common classes, the model achieved a higher F1-score with 2000 additional images of a 64 \(\times\) 64 image size. Additionally, incorporating noise during training reduced errors and improved the training phase. The proposed technique was demonstrated to greatly improve the effectiveness of the model in detecting if motorcyclists were wearing helmets. Also, test time augmentation greatly contributed to helmet violation detection. While mosaic data augmentation works in the training stage, adding an extra layer of augmentation at the test stage substantially improved the performance in conditions of poor lighting and bad weather conditions. This could thereby make detection systems more adaptable and effective in a wider range of scenarios. As the field of computer vision continues to evolve and new YOLO architectures improve upon previous versions, there remains a substantial equity concern. The majority of the datasets used in YOLO come from very organized traffic environments, with higher image qualities and more readily available data. This makes adopting the existing frameworks more challenging in less organized environments, where image quality may be lower, the road environment may be more complex, and additional data may not be available. In these cases, the DCGAN approach allows generating data classes which are missing. This helps addressing this equity concern by considering diverse needs and obstacles faced by various communities worldwide. Several opportunities for future work can be derived from this study. For example, the proposed framework might be improved by incorporating a tracking algorithm, which can track helmet detections over several images to improve the model accuracy. This can further be achieved by using ensembling. Also, future research can explore the impact of synthetic image size and quality by training synthetic images of different sizes and comparing with other real data benchmarks. This enables obtaining a wider collection of traffic scenarios and thus allows for developing models for various environments. Furthermore, exploring the use of other GAN models, such as BigGAN, StyleGAN, and MGAN can provide potential improvements in model performance. Finally, there is great potential to incorporate diffusion models, allowing for further styles to be applied to the generated images. Conceptualization, M.S. and T.G. ; methodology, M.S. and T.G. ; software, M.S. and G.L. ; validation, T.A., G.L., S.G. and T.S. ; formal analysis, S.G. and T.S. ; investigation, T.A. and M.S. ; resources, T.G. ; data curation, T.G. and M.S. ; writing--original draft preparation, M.S., T.G., G.L., T.A., S.G. and T.S. ; writing--review and editing, G.L., T.A. and S.G.; visualization, M.S., T.G. and G.L. ; supervision, S.G. and T.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding.