Abstract: Timely detection and treatment of road cracks are crucial to prevent further deterioration of pavement. An accurate road crack detection algorithm can significantly reduce the human ...

UQLM provides a suite of response-level scorers for quantifying the uncertainty of Large Language Model (LLM) outputs. Each scorer returns a confidence score between 0 and 1, where higher scores ...

The LandingAI Agentic Document Extraction API pulls structured data out of visually complex documents—think tables, pictures, and charts—and returns a hierarchical JSON with exact element locations.

Abstract: Object detection is a foundation process in computer vision having widespread applications in autonomous driving, medical diagnostics and security monitoring. Recent advancements and ...

Abstract: Detecting road cracks is crucial for ensuring road traffic safety and stability. However, currently existing detection methods do not usually pay close attention to the global, local and ...

Abstract: Railway infrastructure safety remains a critical concern in modern transportation systems. This paper presents a novel approach to enhancing rail way safety through the integration of ...

Abstract: Pavement crack detection poses a formidable challenge due to the intricate texture structures of cracks and the complex environmental settings in which they are situated. In recent years, ...

Abstract: Addressing the challenge of surface defect detection in load-bearing rails within auto-motive assembly workshops, which operate in complex environments and under long-term service, this ...

Abstract: Detecting defects in railway tracks, particularly small cracks or gaps, is traditionally a labor-intensive task. By leveraging machine learning, this process can be automated and accelerated ...