News
[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
|
Research
I'm interested in computer vision, machine learning, deep learning and multi-modal models.
|
|
Saam-Reflectnet: Sign-Aware Attention-Based Multitasking Framework for Integrated Traffic Sign Detection and Retroreflectivity Estimation
Joshua Kofi Asamoah, Blessing Agyei Kyem, Nathan-David Obeng-Amoako, Armstrong Aboah
SSRN
This research involves designing a deep learning framework, called Saam-ReflectNet, that combines traffic
sign detection, classification, and retroreflectivity estimation into a single workflow.
It utilizes RGB imagery and LiDAR intensity data with an attention-based backbone to enable scalable
and accurate traffic sign assessments.
|
|
Context-CrackNet: A Context-Aware Framework for Precise Segmentation of Tiny Cracks in Pavement images
Blessing Agyei Kyem, Joshua Kofi Asamoah, Armstrong Aboah
arXiV, 2024
arXiv /
SSRN
This study introduces Context-CrackNet, a novel architecture combining Region-Focused
Enhancement Module (RFEM) and Context-Aware Global Module (CAGM) to improve crack segmentation.
It outperformed state-of-the-art models across ten datasets in accuracy and efficiency,
enabling real-time pavement monitoring.
|
|
Weather-Adaptive Synthetic Data Generation for Enhanced Power Line Inspection Using StarGAN
Blessing 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. The study demonstrates that integrating these synthetic images with real data enhances the performance of object detection models, improving robustness and accuracy under diverse scenarios.
|
|
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, Armstrong Aboah
arXiv, 2024
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)
|
|
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, Kenneth Adomako Tutu, Armstrong Aboah
arXiv, 2024
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. In addition, we develop a web-based platform to enable real-time pavement distress detection for these developing countries.
|
|