TY - JOUR
T1 - Making accurate object detection at the edge
T2 - review and new approach
AU - Huang, Zhenhua
AU - Yang, Shunzhi
AU - Zhou, Meng Chu
AU - Gong, Zheng
AU - Abusorrah, Abdullah
AU - Lin, Chen
AU - Huang, Zheng
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China under Grant 61772366, Grant 62072192, and the Natural Science Foundation of Shanghai under Grant 17ZR1445900. The Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia has funded this project, under grant no. (FP-51-43)
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Nature B.V.
PY - 2022/3
Y1 - 2022/3
N2 - With the development of Internet of Things (IoT), data are increasingly appearing at the edge of a network. Processing tasks at the network edge can effectively solve the problems of personal privacy leakage and server overloading. As a result, it has attracted a great deal of attention. A number of efficient convolutional neural network (CNN) models are proposed to do so. However, since they require much computing and memory resources, none of them can be deployed to such typical edge computing devices as Raspberry Pi 3B+ and 4B+ to meet the real-time requirements of user tasks. Considering that a traditional machine learning method can precisely locate an object with a highly acceptable calculation load, this work reviews state-of-the-art literature and then proposes a CNN with reduced input size for an object detection system that can be deployed in edge computing devices. It splits an object detection task into object positioning and classification. In particular, this work proposes a CNN model with 44 × 44-pixel inputs instead of much more inputs, e.g., 224 × 224-pixel in many existing methods, for edge computing devices with slow memory access and limited computing resources. Its overall performance has been verified via a facial expression detection system realized in Raspberry Pi 3B+ and 4B+. The work makes accurate object detection at the edge possible.
AB - With the development of Internet of Things (IoT), data are increasingly appearing at the edge of a network. Processing tasks at the network edge can effectively solve the problems of personal privacy leakage and server overloading. As a result, it has attracted a great deal of attention. A number of efficient convolutional neural network (CNN) models are proposed to do so. However, since they require much computing and memory resources, none of them can be deployed to such typical edge computing devices as Raspberry Pi 3B+ and 4B+ to meet the real-time requirements of user tasks. Considering that a traditional machine learning method can precisely locate an object with a highly acceptable calculation load, this work reviews state-of-the-art literature and then proposes a CNN with reduced input size for an object detection system that can be deployed in edge computing devices. It splits an object detection task into object positioning and classification. In particular, this work proposes a CNN model with 44 × 44-pixel inputs instead of much more inputs, e.g., 224 × 224-pixel in many existing methods, for edge computing devices with slow memory access and limited computing resources. Its overall performance has been verified via a facial expression detection system realized in Raspberry Pi 3B+ and 4B+. The work makes accurate object detection at the edge possible.
KW - Convolutional neural networks
KW - Edge computing devices
KW - Efficiency
KW - Facial expression detection
KW - Internet of things
UR - http://www.scopus.com/inward/record.url?scp=85114030173&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85114030173&partnerID=8YFLogxK
U2 - 10.1007/s10462-021-10059-3
DO - 10.1007/s10462-021-10059-3
M3 - Article
AN - SCOPUS:85114030173
SN - 0269-2821
VL - 55
SP - 2245
EP - 2274
JO - Artificial Intelligence Review
JF - Artificial Intelligence Review
IS - 3
ER -