Recent camera technology advancements—ranging from smartphones to drones—have made high-quality imagery collection more accessible for engineering applications. Enhanced camera resolution, frame rates, and mobility, along with the rise of artificial intelligence (AI), have led to innovative methods for assessing pavement conditions. Traditionally, pavement assessments relied on expensive laser systems, restricting scalability. However, integrating advanced computer vision with affordable cameras has facilitated accurate surveys at lower costs. Modern AI algorithms now effectively detect, classify, and quantify pavement distress. The industry has responded to these technological advancements by revising standards for pavement condition surveys. The recent ASTM D3303 standard allows for pavement surveys based on images, requiring only the classification of cracks, thereby simplifying survey processes and reducing the need for manual inspections. In this presentation, we will provide case studies demonstrating the successful use of smartphone cameras for pavement condition data collection. Furthermore, we will present real-life examples of pavement surveys conducted under the ASTM D3303 framework. This presentation shows how blending computer vision with pavement engineering can revolutionize pavement condition surveys to be more scalable and affordable. “After this session, participants will be better able to:
• Summarize and discuss the technical requirements for integrating computer vision systems into existing workflows, including hardware, software, and data management, to optimize maintenance strategies and budget allocations.
• Describe how computer vision algorithms detect and analyze pavement distress, enhancing the accuracy and efficiency of pavement condition assessments.
• Communicate real-world examples of municipalities using computer vision in pavement management, understanding the benefits and challenges.”
Contributor/Source
Kurt Keifer-Chief Operations Officer
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