■全世界年轻人死亡的主要原因之一是车祸,大多数死亡发生在坐在前排乘客座位上的儿童身上,在事故发生时,受到安全气囊的直接撞击,这对13岁以下的儿童来说是致命的。本研究旨在通过使用儿童面部检测系统进行内部监控来提高对这种风险的认识,该系统可提醒驾驶员儿童不应坐在前排乘客座位上。
■该系统包含对收集的数据的处理,深度学习的元素,如迁移学习,微调和面部检测,以一种强大的方式识别儿童的存在,这是通过使用从头开始生成的数据集进行训练来实现的。MobileNetV2架构的使用基于良好的性能,当与此任务的Inception架构比较时,这有助于在树莓派4B上实现最终模型。
■生成的图像数据集由102个空座位组成,71名儿童(0-13岁),96名成年人(14-75岁)。从数据增加来看,成人有2,496张图像,儿童有2,310张图像。没有滑动窗口的面分类给出了98%的准确度和100%的准确度的结果。最后,使用拟议的方法,可以实时检测前排乘客座位上的儿童,每个决策和滑动窗口准则的延迟为1s,达到100%的准确度。
■尽管我们在实验环境中的100%精度有些理想化,因为传感器没有被阳光直射遮挡,也没有部分或完全被运送儿童的车辆中常见的污垢或其他碎片覆盖。本研究表明,在任何汽车上都可以在RaspberryPi4ModelB上实现强大的非侵入性分类系统,以通过深度学习方法(如DeepCNN)检测前座儿童。
UNASSIGNED: One of the main causes of death worldwide among young people are car crashes, and most of these fatalities occur to children who are seated in the front passenger seat and who, at the time of an accident, receive a direct impact from the airbags, which is lethal for children under 13 years of age. The present study seeks to raise awareness of this risk by interior monitoring with a child face detection system that serves to alert the driver that the child should not be sitting in the front passenger seat.
UNASSIGNED: The system incorporates processing of data collected, elements of deep learning such as transfer learning, fine-tunning and facial detection to identify the presence of children in a robust way, which was achieved by training with a dataset generated from scratch for this specific purpose. The MobileNetV2 architecture was used based on the good performance shown when compared with the Inception architecture for this task; and its low computational cost, which facilitates implementing the final model on a Raspberry Pi 4B.
UNASSIGNED: The resulting image dataset consisted of 102 empty seats, 71 children (0-13 years), and 96 adults (14-75 years). From the data augmentation, there were 2,496 images for adults and 2,310 for children. The classification of faces without sliding window gave a result of 98% accuracy and 100% precision. Finally, using the proposed methodology, it was possible to detect children in the front passenger seat in real time, with a delay of 1 s per decision and sliding window criterion, reaching an accuracy of 100%.
UNASSIGNED: Although our 100% accuracy in an experimental environment is somewhat idealized in that the sensor was not blocked by direct sunlight, nor was it partially or completely covered by dirt or other debris common in vehicles transporting children. The present study showed that is possible the implementation of a robust noninvasive classification system made on Raspberry Pi 4 Model B in any automobile for the detection of a child in the front seat through deep learning methods such as Deep CNN.