AI module for Visual Inspection

A forestry enterprise faced persistent issues in managing the accuracy and integrity of its visual inspection processes, especially within its transportation monitoring system. The existing algorithm frequently failed to correctly identify vehicles and license plates, undermining the reliability of the data being captured. A particularly concerning limitation was the system’s inability to recognize images taken from screens, which is often a sign of attempted fraud or tampering.

Moreover, the system struggled with images of varying quality, captured under different lighting conditions and from inconsistent angles. This variability further weakened the effectiveness of inspections and left room for both technical and procedural gaps. Without reliable image processing and analysis, the customer’s ability to ensure accountability and operational transparency was severely limited.

To address these challenges, a comprehensive and forward-looking AI solution was developed. At its core, the system leveraged large multimodal models (LMMs) such as LLaVA, phi4-multimodal, and Mistral, which significantly improved the accuracy of visual content analysis.

The backend was built using Python and FastAPI, with MariaDB serving as the data storage solution. To ensure scalability and performance, the system was designed with multithreaded processing, allowing it to manage multiple tasks concurrently and efficiently.

The implemented AI module significantly improved the efficiency of visual inspection processes. The time required to process photographic reports was reduced by over 70%, which helped streamline operations. The automation of routine tasks also led to a reduced workload for operators.