Publications by authors named "Jin-Chun Piao"
Article Synopsis
- * It introduces Fine-YOLO, a lightweight object detection model that incorporates two innovative structures—Low-Parameter Feature Aggregation (LPFA) and High-Density Feature Aggregation (HDFA)—to improve detection accuracy while reducing computational costs.
- * Fine-YOLO was tested on the EDS dataset, achieving 58.3% accuracy with only 16.1 million parameters, and further validations on the NEU-DET dataset showed 73.1% accuracy, indicating its potential for broader applications beyond security.
Simultaneous localization and mapping (SLAM) is emerging as a prominent issue in computer vision and next-generation core technology for robots, autonomous navigation and augmented reality. In augmented reality applications, fast camera pose estimation and true scale are important. In this paper, we present an adaptive monocular visual-inertial SLAM method for real-time augmented reality applications in mobile devices.
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