Search Results for author: Yaw Adu-Gyamfi

Found 9 papers, 1 papers with code

The 1st Data Science for Pavements Challenge

no code implementations10 Jun 2022 Ashkan Behzadian, Tanner Wambui Muturi, Tianjie Zhang, Hongmin Kim, Amanda Mullins, Yang Lu, Neema Jasika Owor, Yaw Adu-Gyamfi, William Buttlar, Majidifard Hamed, Armstrong Aboah, David Mensching, Spragg Robert, Matthew Corrigan, Jack Youtchef, Dave Eshan

The Data Science for Pavement Challenge (DSPC) seeks to accelerate the research and development of automated vision systems for pavement condition monitoring and evaluation by providing a platform with benchmarked datasets and codes for teams to innovate and develop machine learning algorithms that are practice-ready for use by industry.

A Region-Based Deep Learning Approach to Automated Retail Checkout

no code implementations18 Apr 2022 Maged Shoman, Armstrong Aboah, Alex Morehead, Ye Duan, Abdulateef Daud, Yaw Adu-Gyamfi

Automating the product checkout process at conventional retail stores is a task poised to have large impacts on society generally speaking.

object-detection Object Detection

Mobile Sensing for Multipurpose Applications in Transportation

no code implementations20 Jun 2021 Armstrong Aboah, Michael Boeding, Yaw Adu-Gyamfi

Routine and consistent data collection is required to address contemporary transportation issues. The cost of data collection increases significantly when sophisticated machines are used to collect data.

Deep Learning Frameworks for Pavement Distress Classification: A Comparative Analysis

1 code implementation21 Oct 2020 Vishal Mandal, Abdul Rashid Mussah, Yaw Adu-Gyamfi

In this study, the authors deploy state-of-the-art deep learning algorithms based on different network backbones to detect and characterize pavement distresses.

Classification General Classification +1

Artificial Intelligence Enabled Traffic Monitoring System

no code implementations2 Oct 2020 Vishal Mandal, Abdul Rashid Mussah, Peng Jin, Yaw Adu-Gyamfi

Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts.

Management object-detection +1

Object Detection and Tracking Algorithms for Vehicle Counting: A Comparative Analysis

no code implementations31 Jul 2020 Vishal Mandal, Yaw Adu-Gyamfi

In this paper, the authors deploy several state of the art object detection and tracking algorithms to detect and track different classes of vehicles in their regions of interest (ROI).

object-detection Object Detection

Deep Machine Learning Approach to Develop a New Asphalt Pavement Condition Index

no code implementations28 Apr 2020 Hamed Majidifard, Yaw Adu-Gyamfi, William G. Buttlar

Afterward, YOLO (you look only once) deep learning framework was implemented to train the model using the labeled dataset.

BIG-bench Machine Learning General Classification

Pavement Image Datasets: A New Benchmark Dataset to Classify and Densify Pavement Distresses

no code implementations20 Oct 2019 Hamed Majidifard, Peng Jin, Yaw Adu-Gyamfi, William G. Buttlar

Automated pavement distresses detection using road images remains a challenging topic in the computer vision research community.

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