Work

A survey of lightweight methods for object detection networks

Year: 2025

Type: article

Abstract: As social production technologies develop, object detection becomes vital in sectors such as agriculture, industry, and healthcare. It decreases dependence on manual labour and enhances accuracy and efficiency. However, edge devices confront limitations in computational power, storage, and energy, creating a trade-off between accuracy and model size. To tackle this, academia and industry have proposed solutions including hardware-coordinated acceleration, adaptive task lightweighting, and hybrid compression. This paper reviews research from 2020 to 2025 on lightweight object detection, providing a systematic overview of efficient architecture and model compression techniques, explaining their mechanisms, challenges, and future directions to support ongoing progress.

Source: Array

DOI: 10.1016/j.array.2025.100589

Authors: Jing He, Jianfei Jiang, Changfan Zhang

Institution: Hunan University of Technology

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,680