Wei Zhou

Wei Zhou

Associate Professor at Nanjing University of Science and Technology (NJUST)

About Me

I am now a Associate Professor at School of Automation, Nanjing University of Science and Technology (President and Dean’s team). I received the B.S. degree from Nanjing University of Science and Technology and the Ph.D. degree from the School of Transportation, Southeast University.

As of now, I have published/accepted a total of 36 academic papers, including both journal and conference papers. It is important to note that I have published 14 papers as the first author/corresponding author in top journals, such as IEEE Transactions on Industrial Informatics, IEEE Transactions on Intelligent Transportation Systems and Automation in Construction, with 12 of these papers (first author) in JCR Q1 journals (also within CAS JCR Q1 Top-ranked Journals), including 6 papers published in IEEE TITS as the first author.

Moreover, I also serve as an editorial board member for Digital Transportation and Safety and Journal of Transportation Engineering and Information. Also, I am a reviewer for top journals including IEEE TITS, IEEE TIV, IEEE TVT and IEEE TII!

My research interest mainly includes the application of computer vision technology in the transportation field:

  • 2D/3D vehicle detection and tracking: Focuses on visual sensors and LiDAR for roadside monitoring, targeting deficiencies of existing detection/tracking methods under adverse weather/lighting conditions and complex traffic scenarios; Utilizes GAN generative methods and small object detection techniques to improve existing methods.
  • Accident (road anomaly) detection/prediction: Implements rapid accident detection by constructing (lightweight) deep networks to capture accident appearance features (such as vehicle damage) and motion features (sudden decrease in speed); Achieves accident prediction through group relationship modeling and spatiotemporal reasoning.
  • Few-shot learning & Domain adaptation: Addresses the dependence of deep learning models on large data sets and the difficulty of data collection in some scenarios by researching few-shot learning and domain adaptation methods that require only a small number of samples to train and converge the model.
  • Pedestrian crossing intention prediction&Trajectory prediction: Achieves pedestrian crossing intention recognition and trajectory prediction by modeling the relationships of the group around the pedestrian and the pedestrian’s own attributes (such as posture, appearance, and historical trajectory).
  • Multi-modal large models: Large models, also known as foundation models, possess a wealth of prior knowledge after being trained on vast amounts of data, demonstrating powerful zero-shot and few-shot capabilities. Their potential in the field of traffic perception awaits further exploration.

News

  • [2025-03] My first-authored paper “Data-efficient Object Detection on Construction Sites Using Reweighting Mechanism and Cross-batch Contrastive Learning”, has been accepted by IEEE Transactions on Industrial Informatics (JCR Q1, IF=11.7!)
  • [2024-12] My first-authored paper “Synthesizing Realistic Traffic Events from UAV Perspectives: A Mask-guided Generative Approach Based on Style-modulated Transformer”, has been accepted by IEEE Transactions on Intelligent Vehicles (JCR Q1, IF=14!)
  • [2024-10] My first-authored paper “Teaching Segment-Anything-Model Domain-Specific Knowledge for Road Crack Segmentation From On-Board Cameras”, has been accepted by IEEE Transactions on Intelligent Transportation Systems (JCR Q1, IF=8.5)!
  • [2024-01] My first-authored paper “A Fast and Data-Efficient Deep Learning Framework for Multi-Class Fruit Blossom Detection”, has been accepted by Computers and Electronics in Agriculture (JCR Q1, IF=8.3)!
  • [2023-11] My first-authored paper “All-day Vehicle Detection from Surveillance Videos Based on Illumination-adjustable Generative Adversarial Network”, has been accepted by IEEE Transactions on Intelligent Transportation Systems (JCR Q1, IF=8.5)!
  • [2023-09] My first-authored paper “Pedestrian Crossing Intention Prediction from Surveillance Videos for Over-the-horizon Safety Warning”, has been accepted by IEEE Transactions on Intelligent Transportation Systems (JCR Q1, IF=8.5)!
  • [2023-08] My first-authored paper “Monitoring-based Traffic Participant Detection in Urban Mixed Traffic: A Novel Dataset and A Tailored Detector”, has been accepted by IEEE Transactions on Intelligent Transportation Systems (JCR Q1, IF=8.5)!
  • [2023-07] My first-authored paper “An Appearance-Motion Network for Vision-based Crash Detection: Improving the Accuracy in Congested Traffic”, has been accepted by IEEE Transactions on Intelligent Transportation Systems (JCR Q1, IF=8.5)!