AIoT image analysis for real-time dispatching of shipyard transport devices: A focus on trailers

Abstract

In the shipbuilding industry, the Future of Shipyard (FOS) represents a new paradigm driven by data collection, analysis, and prediction. Among various logistics operations, trailer dispatching remains inefficient due to schedules being fixed days or even weeks in advance. To address this issue, we propose an AIoT-based wireless system that enables real-time trailer status monitoring by transmitting image data, GPS information, edge device status, and AI inference results. The proposed system integrates edge computers, wireless communication, centralized servers, and a deep learning model tailored for binary classification. The trailer's complex operational status is decomposed into two tasks: (1) detecting location and movement via GPS, and (2) classifying loading status of ship components using image analysis. To classify the loading status from images, we evaluated five deep learning models-ResNet, VGG, EfficientNet, ViT, and VAN-based on accuracy and F1 score. Among them, the VGG model achieved the best performance, with 97.35 % accuracy and a 96.9% F1 score, demonstrating its suitability for real-world deployment. To enhance model robustness in harsh industrial environments and varying device installation positions, we applied geometric augmentation and validated its effectiveness through additional experiments. Based on this AIoT wireless system, we introduce an innovative dispatch process that replaces manual, experience-based decision-making with data-driven intelligence. This leads to reductions in labor costs and process time, offering meaningful improvements for shipyard operations. The proposed framework also serves as a scalable reference for AIoT applications across broader industrial domains.