Blessing Agyei KyemI am a second 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, Agentic AI and their applications in Transportation and Pavement Engineering. I also conduct research in autonomus driving especially cooperative perception in Vehicle-to-Everything (V2X) Systems. My PhD advisor is Armstrong Aboah. |
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News[03/12/2026] Our paper Hybrid Congestion Classification Framework Using Flow-Guided Attention and Empirical Mode Decomposition has now been accepted for publication at Journal of Transportation Engineering, Part A: Systems [02/01/2026] Our paper Advancing pavement distress detection in developing countries: A novel deep learning approach with locally-collected datasets has now been accepted for publication at Advances in Data Science and Adaptive Analysis journal [10/31/2025] PaveCap has now been accepted for publication at the ASCE Journal of Transportation Engineering, Part B: Pavements [10/05/2025] Our paper titled A Memory-Augmented Transformer with Multi-Scale Spatio-Temporal Modelling for Network-Wide Traffic Flow Prediction has been accepted for presentation at TRB 2026 [10/03/2025] Our paper Self-Supervised Multi-Scale Transformer with Attention-Guided Fusion For Efficient Crack Detection has been accepted for publication at Automation in Construction [09/30/2025] Demographics-Informed Neural Network for Multi-Modal Spatiotemporal Forecasting of Urban Growth and Travel Patterns Using Satellite Imagery has been accepted for presentation at TRB 2026 [09/29/2025] Our paper Demographics-Informed Neural Network for Multi-Modal Spatiotemporal Forecasting of Urban Growth and Travel Patterns Using Satellite Imagery has been accepted In NeurIPS 2025 Workshop on UrbanAI! [09/28/2025] Our paper Self-Supervised Multi-Scale Transformer with Attention-Guided Fusion For Efficient Crack Detection has been accepted for presentation at the TRB 2026 Conference [09/28/2025] Context-CrackNet: A Context-Aware Framework for Precise Segmentation of Tiny Cracks in Pavement Images has been accepted for presentation at the TRB 2026 Conference [09/20/2025] Our paper An Enhanced Lightweight Model for Real-time Pavement Condition Index Prediction has been accepted for presentation at the TRB 2026 Conference [07/12/2025] Three of our research papers have been accepted at ICCV 2025! [04/29/2025] Our research paper: Saam-Reflectnet: Sign-Aware Attention-Based Multitasking Framework for Integrated Traffic Sign Detection and Retroreflectivity Estimation has been accepted for publication at Expert Systems with Applications [04/29/2025] Our research paper: Context-CrackNet: A Context-Aware Framework for Precise Segmentation of Tiny Cracks in Pavement Images has been accepted for publication at Construction and Building Materials [02/26/2025] Our paper A Big Data Approach to Pavement Distress Detection has been accepted for oral presentation at the International Conference on Big Data Analytics 2025 [02/26/2025] Our paper Integrating Travel Behavior Forecasting and Generative Modeling for Predicting Future Urban Mobility and Spatial Transformations has been accepted for oral presentation at the International Conference on Big Data Analytics 2025 [12/19/2024] Our paper titled Weather-Adaptive Synthetic Data Generation for Enhanced Power Line Inspection Using StarGAN has been accepted for publication at IEEE Access [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 |
ResearchI'm interested in deep learning, computer vision, LLMs, multimodal LLMs, autonomous driving, and agentic ai. |
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Task-Specific Dual-Model Framework for Comprehensive Traffic Safety Video Description and AnalysisBlessing Agyei Kyem, Neema Jakisa Owor, Andrews Danyo, Joshua Kofi Asamoah, Eugene Denteh, Tanner Muturi, Anthony Dontoh, Yaw Adu-Gyamfi, Armstrong Aboah ICCV, 2025 pdf / arXiv / slides / post / We propose a dual-model pipeline for traffic-scene video understanding that narrates safety-relevant events and answers questions about them using multimodal LLMs. |
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Prompt-Guided Spatial Understanding with RGB-D Transformers for Fine-Grained Object Relation ReasoningTanner Muturi, Blessing Agyei Kyem, Joshua Kofi Asamoah, Neema Jakisa Owor, Richard Dyzinela, Andrews Danyo, Yaw Adu-Gyamfi, Armstrong Aboah ICCV, 2025 pdf / arXiv / post We present a prompt-guided RGB-D multimodal LLM that performs fine-grained spatial reasoning in warehouse environments. It integrates object bounding boxes and depth cues into textual prompts to improve relation reasoning, distance estimation, and spatial grounding. |
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A Unified Detection Pipeline for Robust Object Detection in Fisheye-Based Traffic SurveillanceNeema Jakisa Owor, Joshua Kofi Asamoah, Tanner Wambui Muturi, Anneliese Jakisa Owor, Blessing Agyei Kyem, Andrews Danyo, Yaw Adu-Gyamfi, Armstrong Aboah ICCV, 2025 pdf / arXiv / post We propose a unified fisheye objection detection pipeline that combines fisheye-aware preprocessing, super-resolution/slicing postprocessing, and an ensemble of modern detectors to robustly detect small and distorted traffic objects. |
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Self-supervised multi-scale transformer with Attention-Guided Fusion for efficient crack detectionBlessing Agyei Kyem, Joshua Kofi Asamoah, Eugene Denteh, Andrews Danyo, Armstrong Aboah Automation in Construction, 2025 paper / arXiv / code / poster / bibtex We present Crack-Segmenter, a self-supervised multi-scale transformer for pixel-level crack segmentation. |
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Saam-Reflectnet: Sign-Aware Attention-Based Multitasking Framework for Integrated Traffic Sign Detection and Retroreflectivity EstimationJoshua Kofi Asamoah, Blessing Agyei Kyem, Nathan-David Obeng-Amoako, Armstrong Aboah Expert Systems with Applications, 2025 paper / SSRN / bibtex This research involves designing a multi-tasking framework, called Saam-ReflectNet, that combines traffic sign detection, classification, and retroreflectivity estimation into a single workflow. |
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Context-CrackNet: A Context-Aware Framework for Precise Segmentation of Tiny Cracks in Pavement imagesBlessing Agyei Kyem, Joshua Kofi Asamoah, Armstrong Aboah Construction and Building Materials, 2025 paper / arXiv / code / poster / bibtex This study introduces Context-CrackNet, a novel architecture combining Region-Focused Enhancement Module (RFEM) and Context-Aware Global Module (CAGM) to improve crack segmentation. |
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Weather-Adaptive Synthetic Data Generation for Enhanced Power Line Inspection Using StarGANBlessing Agyei Kyem, Joshua Kofi Asamoah, Armstrong Aboah, Ying Huang IEEE Access, 2024 paper / bibtex This research introduces a framework combining heuristic image processing and StarGAN to generate synthetic power line inspection images under adverse weather conditions like fog, rain, and nighttime. |
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PaveCap: The First Multimodal Framework for Comprehensive Pavement Condition Assessment with Dense Captioning and PCI EstimationBlessing Agyei Kyem, Eugene Kofi Okrah Denteh, Joshua Kofi Asamoah, Armstrong Aboah Journal of Transportation Engineering, Part B: Pavements, 2026 paper / arXiv / code / poster / website / 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) |
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Advancing Pavement Distress Detection in Developing Countries: A Novel Deep Learning Approach with Locally-Collected DatasetsBlessing Agyei Kyem, Eugene Kofi Okrah Denteh, Joshua Kofi Asamoah, Kenneth Adomako Tutu, Armstrong Aboah Advances in Data Science and Adaptive Analysis, 2026 paper / arXiv / website / 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. |
Released Datasets
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Honors & Awards
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ServiceI have reviewed 60+ manuscripts across the following journals and conferences. Journal Reviewer
Conference Reviewer
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CoursesSpring 2026 (In Progress)
Fall 2025
Spring 2025
Fall 2024
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Design and source code from Jon Barron's website |