A survey of lightweight methods for object detection networks

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.