Blessing Agyei Kyem

I am a first year PhD student at the Sustainable Mobility and Advanced Research in Transportation (SMART) Lab at North Dakota State University, where I work on Multi-modal AI in Computer Vision and its applications in Transportation and Pavement Engineering. My PhD advisor is Armstrong Aboah.

From 2019 to 2022, I worked at Bismuth Technologies as a Data Scientist where I led their Data Team. I contributed to making data-driven decisions by developing predictive models, optimizing data pipelines, and providing actionable insights that significantly improved product performance and customer satisfaction .

I have a BS in Civil Engineering at Kwame Nkrumah University of Science and Technology, where I worked in Kenneth Adomako-Tutu's lab. After my undergrad, I was a research assistant at the Transport Research and Education Centre KNUST and a teaching assistant for Augustus Ababio-Donkor (CE 367).

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News

[09/25/2024]         Our research paper titled A novel methodological framework for assessing traffic sign retroreflectivity using Lidar data has been accepted for presentation at the TRB 2025 Conference

[09/22/2024]         Our paper PaveCap: The First Multimodal Framework for Comprehensive Pavement Condition Assessment with Dense Captioning and PCI Estimation has been accepted for presentation at the TRB 2025 Annual Conference Meeting

Research

I'm interested in computer vision, machine learning, deep learning and multi-modal models.

PaveCap: The First Multimodal Framework for Comprehensive Pavement Condition Assessment with Dense Captioning and PCI Estimation

We show how to provide dense caption describing the condition of a pavement image. The textual description captures the types of pavement distress present with their severity, the absent pavement distresses and the pavement condition index (PCI)

Advancing Pavement Distress Detection in Developing Countries: A Novel Deep Learning Approach with Locally-Collected Datasets

This research presents a deep learning approach combining YOLOv5 with Convolutional Block Attention Module (CBAM) to detect and classify pavement distress types in developing countries. In addition, we develop a web-based platform to enable real-time pavement distress detection for these developing countries.

Design and source code from Jon Barron's website