Lightweight Deep Learning for Visual Perception: A Survey of Models, Compression Strategies, and Edge Deployment Challenges

The increasing demand for the deployment of deep neural networks (DNNs) in edge devices has led to the development of lightweight deep learning (LDL) models designed to operate efficiently under resource constraints. Although DNNs have achieved remarkable success in various applications, their high computational requirements often limit their deployment on devices with restricted memory and processing power. This challenge has motivated researchers to develop optimized LDL models that balance accuracy, speed, and efficiency while maintaining competitive performance. Despite existing surveys covering specific aspects of LDL models, a comprehensive review encompassing image classification, object detection, and segmentation remains limited. This proposed survey systematically explores recent advancements in LDL models, highlighting their architectures, optimization techniques, and real-world applications. This survey conducts an empirical evaluation by testing latest state-of-the-art LDL models on the Jetson Orin edge device using benchmark datasets: Caltech-256 for classification, VisDrone for object detection, and COCO for segmentation. The experimental analysis focuses on key performance metrics, including inference speed, model accuracy, and computational efficiency, while comparing LDL models with their conventional counterparts. This study provides a holistic understanding of the role of LDL models in edge computing, providing insight into emerging trends, challenges, and future research directions in the field.