The availability of techniques for driver distraction detection has been difficult to put to use because of delays caused due to lag in inferencing the model. Distractions caused due to handheld devices have been major causes of traffic accidents as they affect the decision-making capabilities of the driver and gives them less time to react to difficult situations. Often drivers try to multitask which reduces their reaction time leading to accidents, which can easily be avoided if they had been attentive. As such, problems related to the driver’s negligence towards safety a possible solution is to monitor the driver and driving behavior and alerting them if they are distracted. In this paper, we propose a novel approach for detecting when a driver is distracted due to in hand electronic devices which is not only able to detect the distraction with high accuracy but also is energy and memory efficient. Our proposed compressed neural got an accuracy of 0.83 in comparison to 0.86 of heavyweight network.

中文翻译：

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压缩能量高效卷积神经网络的分散驾驶员检测

由于推理模型的滞后引起的延迟，难以使用驾驶员注意力分散检测技术。手持设备引起的干扰已成为交通事故的主要原因，因为它们会影响驾驶员的决策能力，并给他们更少的时间来应对困难的情况。驾驶员通常会尝试执行多任务，以减少导致事故的反应时间，如果他们专心工作，则很容易避免。因此，与驾驶员对安全疏忽相关的问题，一种可能的解决方案是监视驾驶员和驾驶行为，并在驾驶员分心时对其进行警告。在本文中，我们提出了一种新颖的方法来检测驾驶员何时由于手持电子设备而分心，该方法不仅能够以高精度检测分心，而且还具有节能和存储效率。与重量级网络的0.86相比，我们提出的压缩神经的精度为0.83。