Design of Image Semantic Communication System Based on Deep Learning

The rapid advancement of industrial automation and the Industrial Internet of Things (IIoT) has raised new requirements for intelligent and bandwidth-efficient image transmission and analysis methods. Traditional communication systems often struggle with limited bandwidth, high latency, and reduced robustness under complex environments. To address these challenges, we propose a deep learning-based image semantic communication system designed for visual quality inspection in industrial scenarios. The proposed system leverages a Visual Question Answering-based Industrial Semantic Communication System (VQA-ISCS), which integrates semantic encoding, multimodal analysis, and intelligent question answering. Production line images are captured by industrial cameras, and semantic features are extracted through deep encoders. These features are transmitted over constrained channels to a cloud server, where image content is interpreted and inspection-related queries are answered by a multimodal model. The generated responses guide real-time defect elimination or alert actions via a control unit. Experimental results demonstrate that the proposed VQAISCS achieves superior communication efficiency and semantic accuracy under diverse channel conditions.