Edge Runners 3.2

Start

Hulk

Shahid9455

EdgeAI Vision is an innovative solution designed to enhance infrastructure analysis through advanced artificial intelligence (AI) models. As infrastructure systems become increasingly complex, detecting faults and issues such as damaged cables, faulty switches, or compromised wires has never been more critical. Traditional manual inspection methods are often time-consuming, prone to human error, and inefficient, leading to delayed repairs and increased downtime.

Our project leverages the power of Llama 3.2 90B Vision Instruct Turbo, a state-of-the-art AI model that enables real-time image analysis. By allowing users to upload images of their infrastructure, the application can automatically identify and diagnose problems with precision. EdgeAI Vision goes beyond basic detection by providing users with a comprehensive analysis of the detected issues, including potential causes of damage, impacts on overall functionality, and actionable recommendations for repair.

With features such as detailed fault localization, the ability to highlight specific components, and enhanced accuracy through network integration, EdgeAI Vision revolutionizes how infrastructure maintenance is approached. The integration of AI not only speeds up the diagnostic process but also improves the reliability of the findings, thereby reducing the likelihood of future failures.

As we look to the future, EdgeAI Vision aims to expand its capabilities further by integrating live monitoring systems and supporting larger datasets for training. This evolution will empower users to tackle complex infrastructure challenges in real-time, particularly in underserved areas where traditional resources may be limited. Through EdgeAI Vision, we strive to ensure that infrastructure is maintained at the highest standards, ultimately enhancing safety, reliability, and operational efficiency.

AI-driven solution that analyzes infrastructure images for real-time fault detection. Utilizing the Llama 3.2 model, it identifies issues like damaged cables or faulty switches, providing detailed insights and actionable repair recommendations.

"Hulk team on Edge Runners 3.2 Hackathon"

Team Idea

Submission