Near-Edge Computing Aware Object Detection: A Review

Abstract

Object detection is an important task required in computer vision to solve many problems. This is because humans have very good performance in object detection task, compared to machines, hence the need to improve computer vision. Object detection consists of two sub tasks, namely localization of the object and classification of the object. Object detection is slow due to high computing capacity demand of both sub tasks. Consequently, segmentation and classification performance are often slow, even when deployed on high-capacity computing platforms such as CPU-GPU combinations. Edge devices are constrained by limited computational power, which results in poor object detection performance. The implementation of near-edge computing is used particularly in applications such as drone surveillance and autonomous vehicles, where real-time decision-making is essential despite the inherent limitations in computing power. Current object detection methods such as Regional Convolutional Neural Network (RCNN) and You Only Look Once (YOLO) rely on millions of weight parameters, typically stored in two, four, or eight bytes. Therefore, the algorithms need to store the weight parameters in big size of memory and when the operation of the weight parameters and the input signal are executed, longer bytes of data require higher computational load. The huge volume of operations required lead to slower computation. To address this issue, it is necessary to simplify the object detection models, through the reduction of the byte size and the number weight parameter. The challenges of implementing object detection in near edge computing devices is providing the lightweight the object detection models while maintaining the precision and accuracy. This review discusses the current state of object detection methods, object detection simplification methods including deep learning compression techniques, the previous effort undertaken research community to mitigate these challenges, and the associated limitations. This paper also discusses some simplification techniques carried out such as regional proposal network (RPN) replacement, backbone compression and head modification. Some of the object detection model simplification will target the near edge devices with Central Processing unit (CPU) and Graphical Processing Unit (GPU) through varying memory configuration and clock speed. In general, object detection model simplification will lead to an increase in speed with a consequences of mean average precision (mAP) drop. The remaining challenge is to find the best suitable model compression in order to achieve the speed and maintain high mAP.

Topics & Concepts