A Low-Latency Object Detection Algorithm for the Edge Devices of IoV Systems

• Published in: IEEE transactions on vehicular technology Vol. 69; no. 10; pp. 11169 - 11178
• Main Authors: Dai, Cheng, Liu, Xingang, Chen, Weiting, Lai, Chin-Feng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Automobile safety; Cloud computing; Computational modeling; Edge computing; Electronic devices; Feature extraction; frame similarity compression; Frames (data processing); internet of vehicle; latency reduction; Meteorological satellites; Network latency; Object detection; Object recognition; Reduction; Task analysis; Vehicles; Vision
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2020.3008265

The emergence of edge computing (EC) and intelligent vision-based driver assistance system is of great significance for the prospective development of Internet of Vehicle (IoV). The additional computation capability and extensive network coverage provides energy-limited smart devices with more opportunities to enable IoV system for time-sensitive applications. However, when implemented in a vision-based driver assistance system, the transmission of a large amount of redundant data not only causes delay but also severely compromises the accuracy of object detection. In this paper, an improved object detection algorithm based on video key-frame for latency reduction on edge IoV system is proposed. It can significantly improve latency reduction performance at the expense of small detection accuracy. In our proposal, we adopt an important coefficient and frame similarity comparison algorithm to filter redundant frames and achieve key frames for object detection. Then an improved Haar-like feature based classification algorithm is used for object detection under the edge computation model. Finally, a scalable cluster object detection system is implemented as a practical EC case to verify our proposal, and extensive simulations confirm the superiority of the proposed scheme over regular schemes. It can speed up about 84 times with 40% of the similar frames filtered in comparison.