Research Article
An Multi-feature Fusion Object Detection System for Mobile IoT Devices and Edge Computing
@INPROCEEDINGS{10.1007/978-3-030-66922-5_23, author={Xingyu Feng and Han Cao and Qindong Sun}, title={An Multi-feature Fusion Object Detection System for Mobile IoT Devices and Edge Computing}, proceedings={Security and Privacy in New Computing Environments. Third EAI International Conference, SPNCE 2020, Lyngby, Denmark, August 6-7, 2020, Proceedings}, proceedings_a={SPNCE}, year={2021}, month={1}, keywords={Object detection Deep learning SIFT IoT}, doi={10.1007/978-3-030-66922-5_23} }- Xingyu Feng
Han Cao
Qindong Sun
Year: 2021
An Multi-feature Fusion Object Detection System for Mobile IoT Devices and Edge Computing
SPNCE
Springer
DOI: 10.1007/978-3-030-66922-5_23
Abstract
With the increase of data scale and computing power, deep learning algorithm has made a prominent breakthrough in computer vision and other complex problems. However, its high complexity and large memory requirements make it very difficult to run in real time on the Internet of things terminal mobile devices. There is still delay the employing of cloud services cannot meet the real-time requirement. With the popularity of mobile terminal devices and the development of Internet of things, it is of great significance to design a real-time deep learning algorithm on IOT edge mobile devices with limited computing and memory resources. This paper proposes a new object detection method based on the current state-of-the-art object detection deep network model RetinaNet and traditional feature extraction method SIFT. RetinaNet is a one-stage detector with excellent detection speed and accuracy. We use RetinaNet as the object location method, then extract the CNN features and SIFT features of the fixed position image and combine them to train a new classifier. The object classification result will be based on the final classifier.
