Joshua Kofi AsamoahI am a PhD student at North Dakota State University, and a part of the Sustainable Mobility and Advanced Research in Transportation (SMART) Lab research group. Under the mentorship of Professor Armstrong Aboah, my research is focused on machine learning, deep learning, computer vision, and Internet of Things (IoT), particularly their applications in autonomous navigation and perception.. My current research project involves predicting lane intentions and vehicle trajectories using Naturalistic driving data. This work employs advanced computer vision and machine learning techniques to analyze real-world driving scenarios. Additionally, I'm exploring IoT integration to improve autonomous systems' perception, enabling safer and more efficient navigation in complex environments.
I have a BS in Civil Engineering from Kwame Nkrumah University of Science and Technology, where I worked in Dr. Jack Banahene's lab. Upon completion, I worked as a research and teaching assistant for Dr. Russell Afrifa.
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News
[05/02/2025] 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: 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[09/21/2024] 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 [07/08/2024] Congratulations! The SMART Lab received a seed grant from AI SUSTEIN. [07/02/2024] Congratulations! The SMART Lab was awarded an EDRF grant. [06/10/2024] Excited to join the SMARTLab. |
Research InterestsI am broadly interested in machine learning, computer vision, and autonomous systems, with a specific focus on developing perception and prediction models for transportation safety and smart infrastructure. |
Research ActivitiesThese include published work, side projects and unpublished research work. |
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Saam-Reflectnet: Sign-Aware Attention-Based Multitasking Framework for Integrated Traffic Sign Detection and Retroreflectivity EstimationJoshua K. Asamoah, Blessing Agyei Kyem. Armstrong Aboah Expert Systems with Applications, 2025 paper / arxiv / We developed SAAM-ReflectNet, a deep learning framework that unifies traffic sign detection, classification, and retroreflectivity estimation into a single automated pipeline. |
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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, (Not Online Yet) 2025. arxiv / This study proposes Context-CrackNet, a novel encoder-decoder architecture featuring the Region-Focused Enhancement Module (RFEM) and Context-Aware Global Module (CAGM). These innovations enhance the model's ability to capture fine-grained local details and global contextual dependencies, respectively. |
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Weather-Adaptive Synthetic Data Generation for Enhanced Power Line Inspection Using StarGANBlessing Agyei Kyem,Joshua Kofi Asamoah, Armstrong Aboah IEEE Access, 2024. paper / This paper addresses this limitation by introducing a novel framework for generating synthetic power line images under diverse weather conditions, thereby enhancing the diversity and robustness of power line inspection systems. The proposed approach employs a combination of novel heuristic image processing techniques and a multi-domain Generative Adversarial Network (GAN) called StarGAN-v2. |
Course Works |
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Design and source code from Jon Barron's website |