Systematic study of lightweight for object detection

Object detection is a critical task in computer vision that has been studied for many years. However, existing object detection models are often computationally expensive and require high-end hardware to achieve real-time performance. To address this issue, many efforts aim to propose lightweight detection deep models to meet the requirements of resource-critical applications. YOLO is such a representative work that relies on a one-stage detection design and a series of optimization strategies. Correspondingly, related object detection works aiming at lightweight requirements based on YOLO are continuously proposed. Nevertheless, there is still a lack of a systematic study on the development of YOLO-based works, making it difficult to understand the lightweight trend for the object detection task. In this paper, we bridge the gap between YOLO's lightweight design and object detection. We comprehensively studied numerous representative works in this field and analyzed their concerns, approaches, performance, and application scenarios in detail. Moreover, we discussed the impacts of these efforts from multiple dimensions and proposed future directions for lightweight detection. Our study and findings are expected to provide valuable suggestions for object detection in low-power and resource-intensive devices.