Graph Neural Network-Driven Computer Vision Approaches for Real-Time Object Detection and Scene Interpretation

Abstract

Graph Neural Networks (GNNs) have emerged as a significant advancement in computer vision by enabling relational and contextual learning for tasks such as real-time object detection and scene understanding. Unlike conventional Convolutional Neural Networks (CNNs), which primarily capture local spatial information, GNNs model interactions among objects and learn complex dependencies within visual scenes. This capability improves semantic reasoning and enhances understanding of dynamic environments with multiple interacting objects. The proposed GNN-based framework combines convolutional feature extraction with graph-based representation learning. In this approach, detected objects are represented as graph nodes, while their relationships are modeled as edges. Through graph propagation, attention mechanisms, and adaptive feature fusion, the system improves relational learning, object localization, and classification accuracy in real time. Experimental studies demonstrate that GNN-enhanced models outperform traditional CNN-based approaches, particularly in challenging conditions involving occlusion, cluttered backgrounds, and object interactions. Spatial-temporal modeling and graph attention networks further improve scene consistency and contextual interpretation. However, GNN-based systems face challenges including computational complexity, scalability, graph construction overhead, and real-time deployment limitations. Future research will likely focus on lightweight architectures, multimodal learning, explainable reasoning, and edge-efficient implementations for intelligent next-generation vision systems.