no code implementations • 10 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.
Automating the product checkout process at conventional retail stores is a task poised to have large impacts on society generally speaking.
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.
Our approach included creating a detection model, followed by anomaly detection and analysis.
In this study, the authors deploy state-of-the-art deep learning algorithms based on different network backbones to detect and characterize pavement distresses.
Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts.
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).
Afterward, YOLO (you look only once) deep learning framework was implemented to train the model using the labeled dataset.
Automated pavement distresses detection using road images remains a challenging topic in the computer vision research community.