AR Visualization System for Ship Detection and Recognition Based on AI
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Abstract (arXiv)
Augmented reality technology has been widely used in industrial design interaction, exhibition guide, information retrieval and other fields. The combination of artificial intelligence and augmented reality technology has also become a future development trend. This project is an AR visualization system for ship detection and recognition based on AI, which mainly includes three parts: artificial intelligence module, Unity development module and Hololens2AR module. This project is based on R3Det algorithm to complete the detection and recognition of ships in remote sensing images. The recognition rate of model detection trained on RTX 2080Ti can reach 96%. Then, the 3D model of the ship is obtained by ship categories and information and generated in the virtual scene. At the same time, voice module and UI interaction module are added. Finally, we completed the deployment of the project on Hololens2 through MRTK. The system realizes the fusion of computer vision and augmented reality technology, which maps the results of object detection to the AR field, and makes a brave step toward the future technological trend and intelligent application.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 11 references that are in the map. First posted on arXiv 21 Nov 2023.
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9 of 11 references.
References · 9
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
- 2017Mask R-CNNcited by 7,259
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
- 2015SSD: Single Shot MultiBox Detectorcited by 3,125
- 2019R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Objectcited by 193
- 2018A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillationcited by 110
- 2018Rotation-Sensitive Regression for Oriented Scene Text Detectioncited by 88
- 2020PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environmentscited by 56
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