Infrastructure Damage Detection Made Precise
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A leading utility company needed to annotate 250,000 road images for crack detection, facing challenges with accuracy requirements and varying image conditions that impacted consistency. Learn how Aya Data delivered precise annotations through specialized training and enhanced quality control, enabling effective infrastructure maintenance prioritization.
Overview
Our Client is a top utility company focused on extending the lifespan and safety of infrastructure using advanced technology. They wanted to create a computer vision model to detect and classify road cracks. This model would use images from drones, satellites, and ground-based cameras to help engineers and maintenance teams prioritise repairs and improve road safety.
Challenge
The Client needed Aya Data to label 250,000 images of roads with different types of cracks. These images needed to be marked with precise details like bounding boxes and polygons so that the computer vision model could learn to identify and classify cracks accurately.
As the project progressed, several challenges emerged:
1. Increased Accuracy Needed: Road cracks vary widely, and their impact on infrastructure can be significant. Therefore, more precise annotation was needed than initially expected.
2. Handling Large Volumes: The number of images continued to grow, and they were taken under different lighting and weather conditions, making consistent annotation challenging.
Solution
To tackle these challenges, Aya Data:
1. Enhanced Annotation Accuracy: We introduced a thorough quality control process involving human reviewers to ensure every annotation met high precision standards. Our annotators also underwent specialised training to accurately identify and classify various types of road cracks.
2. Scaled the Solution: We improved our annotation platform to handle the increasing volume of images without sacrificing accuracy. The platform was also adapted to consistently process images taken in various lighting and weather conditions.
Results
Thanks to our efforts, the Client developed a highly accurate computer vision model that can detect and classify road cracks effectively. This model enabled maintenance crews to perform timely and targeted repairs, which improved road safety and minimised the impact of cracks on infrastructure. Additionally, our scalable annotation solution saved time and resources, allowing the Client to further refine their model.
Interested in better managing road and infrastructure damage? Contact us today to learn how our precision annotation solutions can help you assess and address issues with accuracy.
Disclaimer: Aya Data is committed to client confidentiality. We do not disclose any specific client or project details, so names and identifiable information are kept anonymous in our case studies.
