Research on fatigue driving detection method based on lightweight convolutional neural network
Traffic safety issues are closely related to everyone's life.This article proposes a lightweight detection model to ad-dress the current lack of real-time and accuracy issues in popular detection models.The object detection network YOLOv4 is im-proved,and the backbone feature extraction network uses MobileNetV3.To significantly reduce the network parameters,a deep separable convolution is adopted to further reduce the number of parameters.By adding attention mechanisms to the enhanced fea-ture extraction network,further optimizing detection speed and accuracy.And by integrating Mosaic data augmentation methods,the detection accuracy of the model is ensured.Finally,the improved network in this article is used to extract facial fatigue features,and PERCLOS determines whether fatigue is present and outputs the results.Through experiments,it was found that the detection accuracy is 92.90%,the mAP index is 85.75%,the parameter quantity is reduced by nearly 95%,and the single frame detection speed is 25.64 ms,which can balance the accuracy and real-time performance of the network.
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