Portrait of Weizheng Wang

Weizheng Wang 王维政

Master's student in Artificial Intelligence and Machine Learning at the University of Adelaide

I study efficient and robust perception for UAVs and edge devices, with a focus on computer vision, infrared-visible learning, multimodal systems, and deployment-aware machine learning.

Before Adelaide, I completed a B.Eng. in Computer Science and Technology at North China Institute of Science and Technology, where I worked on UAV monitoring, object detection, and practical perception systems.

Research interests: computer vision, multimodal learning, UAV perception, edge intelligence, and natural language processing.

Selected Publications

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  1. A Lightweight Thermal Denoising and Occlusion-Robust Infrared Detection Model for Substation Equipment

    W. Wang, J. Wu, and L. Tian. Submitted to ICIP 2026. Under review.

  2. A lightweight insulator defect detection algorithm based on drone images for power line inspection

    J. Wu, W. Wang, et al. Engineering Research Express, 2025. DOI

  3. Real-time monitoring of trucks used in open pit based on aerial video of UAV

    W. Wang, J. Wu, et al. RAIIC, 2024. DOI

Updates

  • Started the Master of Artificial Intelligence and Machine Learning at the University of Adelaide.
  • Submitted work on lightweight thermal denoising and infrared detection to ICIP 2026.
  • Published work on lightweight UAV-based insulator defect detection in Engineering Research Express.

Research Experience

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  1. Huawei MindSpore Open-Source Community

    Research Intern · February–June 2025

    Contributed to MindNLP model integration and fine-tuning workflows for Autoformer, BEiT, and ALBERT, with attention to training stability and reproducibility.

  2. Automatic Inspection and Rescue Based on Drone Nest

    Research Project · January–July 2024

    Developed UAV inspection pipelines using DeepSORT and YOLOv8, with ONNX export for real-time edge inference.

  3. Baiyangdian Ecological IoT Monitoring System

    Research Project · June 2023–January 2024

    Studied efficient truck detection and infrared-visible UAV perception using YOLO-family models under practical latency constraints.

Education

  • University of Adelaide
    Master of Artificial Intelligence and Machine Learning
  • North China Institute of Science and Technology
    B.Eng. in Computer Science and Technology, GPA 90.84/100

Selected Honors

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  • University of Adelaide Global Citizens 30% International Scholarship
  • Principal Investigator, National Undergraduate Innovation Training Program
  • Second Prize, Challenge Cup Science and Technology Invention and Creation Track