# A Lightweight Attention-Based Network towards Distracted Driving Behavior Recognition
## 1 Introduction  
According to the Global Status Report on Road Safety [1] released by the World Health Organization (WHO) in 2018, 1.35 million people die from traffic accidents each year, and this number is still increasing. The Traffic Safety Facts [2] released by National Highway Traffic Safety Administration (NHTSA) in 2017 stated that there are at least 2994 fatal crashes caused by distracted driving. It is reported that with a proper driver monitoring system, the risk of distracted driving will be sharply reduced [3]. Distracted driving is defined as \"a diversion of attention from driving because the driver is temporarily focusing on an object, person, task or event not related to driving\" [4]. Physiological signals, vehicle data, and deep learning are the three main methods to classify distracted driving. [PERSON] et al. [5] proposed a system that could detect inattention using an electrocardiogram (ECG) and surface electromyogram (sEMG) signals. K-nearest neighbor analysis (KNN), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) were involved for classification. [PERSON] et al. [6] studied the different EEG patterns in the driver's lane-keeping behaviors. The accuracy was evaluated based on the support vector machine (SVM) and radial basis function (RBF) kernel function. [PERSON] et al. [7] fused the vehicle sensor data with vision data, and significantly improve the accuracy of detection tasks for distracted drivers. [PERSON] et al. [8] utilized the built-in accelerometer and gyro head inertial sensor to collect the driver's head pose information. Although most proposed methods have achieved desirable performances, themethods based on physiological signals exist physical contact with the driver. The collected information is part of personal privacy. Hence, these methods cannot be widely spread. The methods based on vehicle data require installing high-precision sensors, which increases the cost. It should be noted that methods involving physiological signals and vehicle data are not totally separated with deep learning. Instead, they work as complementary plans in these proposed methods. With the rapid development of deep learning, due to the low cost and high accuracy, deep learning-based approaches of distracted driving behavior recognition will be mainstream [9]. Genetic algorithm [10], ensemble learning [11], skin segmentation [12], hybrid CNN [13], Long Short-Term Memory [14], Multiple-Granularity Middle Network [15], multi-stream CNN [16], faster R-CNN [17], and principal component analysis [18] have recognized some success in distracted driving detection. However, due to the limited resources on edge devices, a desirable real-time method should be with low computation cost while still maintaining high accuracy. It should also be with great adaptability to the environment, so it can remain excellent performance under various unknown circumstances. Aiming at these requirements, this paper proposes a Lightweight Attention-based network (LWANet) and is tested in the field of distracted driving. The main contributions of this paper are summarized as follows:  
1. We propose a lightweight Inverted Residual Attention Module (IRAM) towards the problem that the current lightweight network has relatively low accuracy. IRAM effectively improves the classification accuracy with almost no increase of trainable parameters and computation cost. 2. We embed the depthwise separable convolution into classic VGG16 and optimize the network structure. Regarding the problem that classic CNN cannot be implemented on edge devices due to the high model complexity, LWANet has very few trainable parameters and model size. 3. We utilize subtract mean filter in image preprocessing to further improve the model's environmental adaptivity. 4. Compared with existing state-of-the-art deep learning-based methods, the proposed LWANet has fewer parameters and can be applied to various embedded real-time detection scenarios. The rest of this paper is organized as follows. Section 2 introduces the related work.