Authors

Abstract

Driver drowsiness severely impairs the normal operation of harvesters, leading to casualties and economic losses. Effectively detecting driver drowsiness in harvesters remains a significant challenge. This paper introduces a lightweight convolutional neural network that identifies driver drowsiness in harvester operators by analyzing the driver's eyes, mouth, and head posture. The model comprises a lightweight CNN, a Long Short-Term Memory (LSTM) network, and an attention layer, achieving high efficiency and low latency. Experimental results demonstrate that the CNN-LSTM-Attention model effectively balances accuracy and computational efficiency, enabling rapid and precise drowsiness detection. This approach significantly improves safety during combine harvester operation.

Abstract in Chinese

驾驶员困倦严重影响收获机正常作业，造成人员伤亡和经济损失。对收获机驾驶员困倦的有效检测依然是重大挑战。本文介绍了一种轻量化卷积神经网络，利用驾驶员眼睛、嘴巴以及头部姿态对收获机驾驶员驾驶困倦进行识别。该模型由轻量化的CNN、长短时记忆网络以及一个注意力层组成，具有高效、低延迟的特点。实验结果表明，CNN-LSTM-Attention模型很好的平衡了结果准确性和计算效率，能够快速准确的识别困倦，对收获机驾驶安全产生重大影响。