EffRes-DrowsyNet: A Novel Hybrid Deep Learning Model Combining EfficientNetB0 and ResNet50 for Driver Drowsiness Detection

Highlights What are the main findings? EffRes-DrowsyNet, a hybrid deep learning model integrating EfficientNetB0 and ResNet50, achieved high performance across three benchmark datasets: 97.71% accuracy on SUST-DDD, 92.73% on YawDD, and 95.14% on NTHU-DDD. The model demonstrated strong generalization and reliability across diverse conditions, maintaining high precision and recall on all datasets. What is the implication of the main finding? The hybrid architecture offers an effective balance between computational efficiency and deep visual representation, enabling real-time deployment in driver monitoring systems. Its robustness and adaptability make it a promising solution for integration into safety-critical applications, helping to mitigate fatigue-induced accidents in automotive and related domains. Abstract Driver drowsiness is a major contributor to road accidents, often resulting from delayed reaction times and impaired cognitive performance. This study introduces EffRes-DrowsyNet, a hybrid deep learning model that integrates the architectural efficiencies of EfficientNetB0 with the deep representational capabilities of ResNet50. The model is designed to detect early signs of driver fatigue through advanced video-based analytics by leveraging both computational scalability and deep feature learning. Extensive experiments were conducted on three benchmark datasets—SUST-DDD, YawDD, and NTHU-DDD—to validate the model’s performance across a range of environmental and demographic variations. EffRes-DrowsyNet achieved 97.71% accuracy, 98.07% precision, and 97.33% recall on the SUST-DDD dataset. On the YawDD dataset, it sustained a high accuracy of 92.73%, while on the NTHU-DDD dataset, it reached 95.14% accuracy, 94.09% precision, and 95.39% recall. These results affirm the model’s superior generalization and classification performance in both controlled and real-world-like settings. The findings underscore the effectiveness of hybrid deep learning models in real-time, safety-critical applications, particularly for automotive driver monitoring systems. Furthermore, EffRes-DrowsyNet’s architecture provides a scalable and adaptable solution that could extend to other attention-critical domains such as industrial machinery operation, aviation, and public safety systems.