20 citations · 102 across the 42 of their papers we have counts for
42 papers
A Dynamic Scene Interaction Reasoning Framework for Scene-level Lane-Change Intention and Trajectory Prediction of Multiple Interacting Vehicles
Joshua Kofi Asamoah, Blessing Agyei Kyem, Eugene Denteh +1
Safe motion planning in advanced driver-assistance systems and autonomous vehicles requires an accurate understanding of how the surrounding traffic scene is likely to evolve. Howe…
Multi-Task Crack Foundation Model for Engineering-Reliable Crack Representation and Topology Preservation in Civil Infrastructure
Blessing Agyei Kyem, Joshua Kofi Asamoah, Eugene Denteh +1
Reliable crack assessment requires not only accurate pixel-level masks but also connected crack geometry and confidence estimates that remain stable under domain shift. However, ex…
A Road-Conditioned Traffic Movie Prediction Network with Spatiotemporal and Structure-Consistent Learning
Joshua Kofi Asamoah, Blessing Agyei Kyem, Armstrong Aboah
City-wide traffic forecasting is important for congestion management, route guidance, and intelligent transportation systems, but accurate prediction remains challenging when futur…
Hybrid Congestion Classification Framework Using Flow-Guided Attention and Empirical Mode Decomposition
Eugene Kofi Okrah Denteh, Blessing Agyei Kyem, Joshua Kofi Asamoah +1
Accurate traffic congestion classification requires models that jointly capture roadway scene context and non-stationary traffic motion, yet most prior work treats these requiremen…
Class-Adaptive Cooperative Perception for Multi-Class LiDAR-based 3D Object Detection in V2X Systems
Blessing Agyei Kyem, Joshua Kofi Asamoah, Armstrong Aboah
Cooperative perception allows connected vehicles and roadside infrastructure to share sensor observations, creating a fused scene representation beyond the capability of any single…
Vision-Language Foundation Models for Comprehensive Automated Pavement Condition Assessment
Blessing Agyei Kyem, Joshua Kofi Asamoah, Anthony Dontoh +1
General-purpose vision-language models demonstrate strong performance in everyday domains but struggle with specialized technical fields requiring precise terminology, structured r…
A Contextual Analysis of Driver-Facing and Dual-View Video Inputs for Distraction Detection in Naturalistic Driving Environments
Anthony Dontoh, Stephanie Ivey, Armstrong Aboah
Despite increasing interest in computer vision-based distracted driving detection, most existing models rely exclusively on driver-facing views and overlook crucial environmental c…
PaveSync: A Unified and Comprehensive Dataset for Pavement Distress Analysis and Classification
Blessing Agyei Kyem, Joshua Kofi Asamoah, Anthony Dontoh +3
Automated pavement defect detection often struggles to generalize across diverse real-world conditions due to the lack of standardized datasets. Existing datasets differ in annotat…
A Unified Detection Pipeline for Robust Object Detection in Fisheye-Based Traffic Surveillance
Neema Jakisa Owor, Joshua Kofi Asamoah, Tanner Wambui Muturi +5
Fisheye cameras offer an efficient solution for wide-area traffic surveillance by capturing large fields of view from a single vantage point. However, the strong radial distortion…
Prompt-Guided Spatial Understanding with RGB-D Transformers for Fine-Grained Object Relation Reasoning
Tanner Muturi, Blessing Agyei Kyem, Joshua Kofi Asamoah +5
Spatial reasoning in large-scale 3D environments such as warehouses remains a significant challenge for vision-language systems due to scene clutter, occlusions, and the need for p…
Task-Specific Dual-Model Framework for Comprehensive Traffic Safety Video Description and Analysis
Blessing Agyei Kyem, Neema Jakisa Owor, Andrews Danyo +6
Traffic safety analysis requires complex video understanding to capture fine-grained behavioral patterns and generate comprehensive descriptions for accident prevention. In this wo…
Self-Supervised Multi-Scale Transformer with Attention-Guided Fusion for Efficient Crack Detection
Blessing Agyei Kyem, Joshua Kofi Asamoah, Eugene Denteh +2
Pavement crack detection has long depended on costly and time-intensive pixel-level annotations, which limit its scalability for large-scale infrastructure monitoring. To overcome…
Demographics-Informed Neural Network for Multi-Modal Spatiotemporal forecasting of Urban Growth and Travel Patterns Using Satellite Imagery
Eugene Kofi Okrah Denteh, Andrews Danyo, Joshua Kofi Asamoah +2
This study presents a novel demographics informed deep learning framework designed to forecast urban spatial transformations by jointly modeling geographic satellite imagery, socio…
Visual Dominance and Emerging Multimodal Approaches in Distracted Driving Detection: A Review of Machine Learning Techniques
Anthony Dontoh, Stephanie Ivey, Logan Sirbaugh +2
Distracted driving continues to be a significant cause of road traffic injuries and fatalities worldwide, even with advancements in driver monitoring technologies. Recent developme…
An Improved ResNet50 Model for Predicting Pavement Condition Index (PCI) Directly from Pavement Images
Andrews Danyo, Anthony Dontoh, Armstrong Aboah
Accurately predicting the Pavement Condition Index (PCI), a measure of roadway conditions, from pavement images is crucial for infrastructure maintenance. This study proposes an en…
Integrating Travel Behavior Forecasting and Generative Modeling for Predicting Future Urban Mobility and Spatial Transformations
Eugene Denteh, Andrews Danyo, Joshua Kofi Asamoah +3
Transportation planning plays a critical role in shaping urban development, economic mobility, and infrastructure sustainability. However, traditional planning methods often strugg…
Context-CrackNet: A Context-Aware Framework for Precise Segmentation of Tiny Cracks in Pavement images
Blessing Agyei Kyem, Joshua Kofi Asamoah, Armstrong Aboah
The accurate detection and segmentation of pavement distresses, particularly tiny and small cracks, are critical for early intervention and preventive maintenance in transportation…
A Review Paper of the Effects of Distinct Modalities and ML Techniques to Distracted Driving Detection
Anthony. Dontoh, Stephanie. Ivey, Logan. Sirbaugh +1
Distracted driving remains a significant global challenge with severe human and economic repercussions, demanding improved detection and intervention strategies. While previous stu…
PaveSAM Segment Anything for Pavement Distress
Neema Jakisa Owor, Yaw Adu-Gyamfi, Armstrong Aboah +1
Automated pavement monitoring using computer vision can analyze pavement conditions more efficiently and accurately than manual methods. Accurate segmentation is essential for quan…
Advancing Pavement Distress Detection in Developing Countries: A Novel Deep Learning Approach with Locally-Collected Datasets
Blessing Agyei Kyem, Eugene Kofi Okrah Denteh, Joshua Kofi Asamoah +2
Road infrastructure maintenance in developing countries faces unique challenges due to resource constraints and diverse environmental factors. This study addresses the critical nee…
PaveCap: The First Multimodal Framework for Comprehensive Pavement Condition Assessment with Dense Captioning and PCI Estimation
Blessing Agyei Kyem, Eugene Kofi Okrah Denteh, Joshua Kofi Asamoah +1
This research introduces the first multimodal approach for pavement condition assessment, providing both quantitative Pavement Condition Index (PCI) predictions and qualitative des…
Low-Light Image Enhancement Framework for Improved Object Detection in Fisheye Lens Datasets
Dai Quoc Tran, Armstrong Aboah, Yuntae Jeon +3
This study addresses the evolving challenges in urban traffic monitoring detection systems based on fisheye lens cameras by proposing a framework that improves the efficacy and acc…
Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event Analysis
Maged Shoman, Dongdong Wang, Armstrong Aboah +1
This paper introduces our solution for Track 2 in AI City Challenge 2024. The task aims to solve traffic safety description and analysis with the dataset of Woven Traffic Safety (W…
3D Object Detection and High-Resolution Traffic Parameters Extraction Using Low-Resolution LiDAR Data
Linlin Zhang, Xiang Yu, Armstrong Aboah +1
Traffic volume data collection is a crucial aspect of transportation engineering and urban planning, as it provides vital insights into traffic patterns, congestion, and infrastruc…