摘要
边缘设备的快速发展和深度学习的落地应用越来越多,两者结合的趋势越发明显。而针对低功耗边缘设备AI应用的潜力还未完全开发出来,大量设备隐藏着大量计算能力,释放其潜力所带来的社会效益和经济效益是非常明显的。因此,以目标检测任务中较为常见的人脸检测为例,将MTCNN人脸检测算法改进并移植到资源极其紧张的低功耗嵌入式平台,在一定环境条件下,最终成功地检测到人脸,并绘制出人脸候选框,结合舵机云台具备了一定的人脸跟踪能力。
The rapid development of edge devices and the application of deep learning are increasing,the trend of combining the two is becoming more and more obvious.The potential of AI applications for low-power edge devices has not yet been fully developed.A large number of devices hide a lot of computing power.The social and economic benefits brought by the release of its potential are very obvious.Therefore,taking the more common face detection in objective detection tasks as an example,the MTCNN face detection algorithm is improved and transplanted to a low-power embedded platform with extremely limited resources.Under certain environmental conditions,the face is finally successfully detected,and the face candidate boundingbox is drawn,it has face tracking function combined with the servo.
作者
祁星晨
卓旭升
Qi Xingchen;Zhuo Xusheng(School of Information and Electrical Engineering,Wuhan Institute of Technology,Wuhan 430205,China)
出处
《电子技术应用》
2021年第5期40-44,共5页
Application of Electronic Technique
基金
湖北省自然科学基金(2016CFC757)。
关键词
低功耗边缘设备
目标检测
人脸检测跟踪
级联卷积神经网络
low-power edge devices
object detection
face detection and tracking
cascaded convolutional neural network
