Optimizing YOLOv5 for Green AI: A Study on Model Pruning and Lightweight Networks

To achieve state-of-the-art performance, deep learning models are becoming increasingly complex, leading to a significant increase in demand for high-performance computing resources and, in turn, concerns about environmental impact. In this context, the concept of Green AI has been proposed, which advocates optimizing performance by improving model efficiency, rather than relying solely on an increase in computing resources, thereby reducing the impact on the environment. Object detection is a research hotspot in the field of computer vision. This paper focuses on the commonly used YOLOv5 network in object detection, optimizing the YOLOv5 model through pruning and the use of lightweight networks. Implemented on a campus image dataset, a balance between reducing computational load and maintaining accuracy was achieved. The experimental results confirm that strategic model pruning and thoughtful network architecture selection can produce environmentally responsible and computationally efficient deep learning models without significantly reducing performance, aligning with the goals of GreenAI. All related code of the project is available at: https://github.com/xbgthws/Green-AI-project.git .