Near-Edge Computing Aware Object Detection: A Review

Abstract

Object detection is a widely applied approach in addressing many real-world computer vision challenges. Despite its importance, object detection is computationally intensive and time-consuming, even with advanced CPU-GPU combinations. With the rise of edge computing and smaller AI accelerators, there is an increasing need to deploy efficient object detection applications on near-edge devices, such as drones and autonomous vehicles. However, these applications often face significant challenges and performance limitations due to restricted computational resources. Traditional object detection methods, e.g., Regional Convolutional Neural Network (RCNN) and You Only Look Once (YOLO), have extensive weight parameters, leading to high demands on memory and computing resources. Therefore, it is important to compress and optimize object detection models by reducing both the size and the number of weight parameters. This review article delves into the current state of object detection methods and simplification strategies, with a focus on deep-learning compression techniques. We investigate various approaches to mitigate these computational challenges, including replacing the regional proposal network (RPN), compressing model backbones, and modifying model heads, specifically for near-edge devices with limited and energy-efficient CPUs and GPUs. While simplifying object detection models is expected to reduce processing time significantly, it can also negatively impact model accuracy. Therefore, we discuss the ongoing challenge of finding the optimal model compression that balances speed while maintaining high accuracy.