MobileNet-Based Architecture for Distracted Human Driver Detection of Autonomous Cars

Abstract

Distracted human driver detection is an important feature that should be included in most levels of autonomous cars, because most of these are still under development. Hereby, this paper proposes an architecture to perform this task in a fast and accurate way, with a full declaration of its details. The proposed architecture is mainly based on the MobileNet transfer learning model as a backbone feature extractor, then the extracted features are averaged by using a global average pooling layer, and then the outputs are fed into a combination of fully connected layers to identify the driver case. Also, the stochastic gradient descent (SGD) is selected as an optimizer, and the categorical cross-entropy is the loss function through the training process. This architecture is performed on the State-Farm dataset after performing data augmentation by using shifting, rotation, and zooming. The architecture can achieve a validation accuracy of 89.63%, a validation recall of 88.8%, a validation precision of 90.7%, a validation f1-score of 89.8%, a validation loss of 0.3652, and a prediction time of about 0.01 seconds per image. The conclusion demonstrates the efficiency of the proposed architecture with respect to most of the related work.

An Algorithm for Distracted Driving Recognition Based on Pose Features and an Improved KNN

“…Table 5 compares our algorithm with other algorithms. Paper [36] employed a Mo-bileNet model based on transfer learning as the backbone for feature extraction, achieving approximately 90% accuracy while demonstrating a significant speed advantage with up to 100 FPS. The algorithm described in paper [37] innovatively utilizes ResNet50 for image feature extraction and then employs SVM for classification, resulting in the ReSVM algorithm achieving a high accuracy rate.…”

“…Accuracy/% FPS/Hz Ours 94.04 50 Improved-MobileNet [36] 89.63 100 ReSVM [37] 95.50 -YOLO-LBS [38] 93.80 75…”