Acquiring real-time, accurate, large datasets is crucial and time-consuming for specific problems. Numerous datasets are available with annotations, but most are not feasible for a special task because of differences in the class label, class imbalance, and variability. One such solution to this problem is to use artificially crafted datasets (or synthetic datasets), which are scalable and can be automatically annotated. We utilized two different approaches—stable diffusion and cut-paste-blend—to generate a synthetic dataset. This study investigates the use of synthetic image datasets to observe the performance of YOLOv8 and improved YOLOv8 models for helmet detection. We trained models on both real-world and synthetic datasets and evaluated their performance in terms of detection accuracy. After training 50 epochs, the model achieved a mAP@50 of 78.6% on real data, 45.5% on synthetic data, and 75.4% on hybrid datasets. We analyzed how the hybrid dataset affected results using different ratios and discovered that with a 3:1 mix of hybrid data, the YOLOv8-based model reached an mAP@50 of 90.3%, which is better than when real and synthetic data were used in equal amounts. We proposed the Convolutional Block Attention Module-based YOLOv8-CBAM to enhance the accuracy of helmet and non-helmet detection. Experimental results indicate that YOLOv8-CBAM achieved an mAP@50 of 91% at 50, which is 0.7% better than the baseline model. This study also indicates that the correct proportion of synthetic datasets solved the class imbalance problem and improved the helmet detection accuracy in challenging environments.
Keywords
Synthetic Data, YOLO, Attention Mechanism, Deep Learning, Helmet Detection.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Arshad M, Kumar P;
Methodology: Arshad M, Kumar P;
Software: Arshad M;
Data Curation: Arshad M;
Writing- Original Draft Preparation: Arshad M, Kumar P;
Visualization: Arshad M;
Investigation: Kumar P;
Supervision: Kumar P;
Validation: Arshad M, Kumar P;
Writing- Reviewing and Editing: All authors reviewed the results and approved the final version of the manuscript.
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Author(s) thanks to Dr. Pradeep Kumar for this research completion and support.
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Mohd Arshad
Department of Computer Science and Information Technology, Maulana Azad National Urdu University, Hyderabad, Telangana, India.
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Cite this article
Mohd Arshad and Pradeep Kumar, “Impact of Synthetic Data on Training and Improved YOLOv8 Models for Helmet Detection”, Journal of Machine and Computing, vol.5, no.3, pp. 1439-1449, July 2025, doi: 10.53759/7669/jmc202505114.
