A Computer Vision-Based Approach for Driver Distraction Recognition using Deep Learning and Genetic Algorithm Based Ensemble
This work addresses road safety by improving driver distraction recognition, though it is incremental as it builds on existing techniques.
The paper tackled driver distraction classification by proposing a genetic algorithm-based ensemble of six deep neural networks, achieving accuracies of 96.37% on the AUC Distracted Driver Dataset and 99.75% on the State Farm Driver Distraction Dataset.
As the proportion of road accidents increases each year, driver distraction continues to be an important risk component in road traffic injuries and deaths. The distractions caused by the increasing use of mobile phones and other wireless devices pose a potential risk to road safety. Our current study aims to aid the already existing techniques in driver posture recognition by improving the performance in the driver distraction classification problem. We present an approach using a genetic algorithm-based ensemble of six independent deep neural architectures, namely, AlexNet, VGG-16, EfficientNet B0, Vanilla CNN, Modified DenseNet, and InceptionV3 + BiLSTM. We test it on two comprehensive datasets, the AUC Distracted Driver Dataset, on which our technique achieves an accuracy of 96.37%, surpassing the previously obtained 95.98%, and on the State Farm Driver Distraction Dataset, on which we attain an accuracy of 99.75%. The 6-Model Ensemble gave an inference time of 0.024 seconds as measured on our machine with Ubuntu 20.04(64-bit) and GPU as GeForce GTX 1080.