Portfolio
DAI Labs has extensive experience in AI development, including projects related to image, video, text and time series data analysis. These are some of our highlighted projects, across a wide range of industries.
DAI Labs has extensive experience in AI development, including projects related to image, video, text and time series data analysis. These are some of our highlighted projects, across a wide range of industries.
Our AI project focuses on the development of an advanced solution that identifies and segments trees and shrubs in panoramic images captured by a custom 360-degree camera. Utilizing cutting-edge image processing and machine learning techniques, this system can differentiate between various types of vegetation with high precision. This innovation aims to enhance environmental monitoring, urban planning, and ecological research by providing detailed vegetation analysis.
Accurately identifying and cataloging vegetation types in diverse environments poses a significant challenge due to the complexity of natural landscapes and the limitations of traditional photography. Current methods often require manual intervention and are time-consuming and prone to errors. Our project addresses these issues by automating the process of vegetation identification and segmentation in 360-degree panoramic images, aiming for efficiency and accuracy.
To solve this problem, we designed an AI model that leverages deep learning algorithms to analyze 360-degree images for vegetation identification. The model is trained on a vast dataset of annotated images to recognize patterns and features unique to different types of trees and shrubs. By integrating this model with the client's 360-degree camera system, we achieve automatic and precise vegetation mapping in various settings.
The deployment of our AI solution demonstrated exceptional capability in identifying and segmenting trees and shrubs across a range of landscapes, achieving high accuracy and detail in vegetation mapping. This system significantly reduced the time and effort required for environmental data collection and analysis. Its success opens new avenues for scalable and automated ecological studies, urban planning, and natural resource management.
Our AI project introduces a groundbreaking solution for real-time monitoring and evaluation of the tunnel boring process through video feed analysis. By leveraging advanced machine learning algorithms, this system assesses the quality of drilling, identifies potential issues, and suggests adjustments to optimize the boring machine's operation. This technology aims to enhance efficiency, reduce downtime, and minimize the risk of costly stoppages in tunnel construction projects.
Tunnel boring machines (TBMs) are crucial for efficient tunnel construction, yet their operation is often hampered by unexpected geological challenges and mechanical issues, leading to costly delays and stoppages. Traditional monitoring methods are reactive and rely heavily on manual inspection, which can be slow and sometimes inaccurate. Our project addresses the need for a proactive, automated solution that can continuously assess the drilling quality and predict potential problems before they escalate.
We developed an AI-powered system that analyzes live video feeds from cameras mounted on the TBM to monitor the drilling process in real time. Utilizing computer vision and deep learning techniques, the system identifies anomalies, evaluates drilling performance, and detects signs of wear or malfunction. By providing instant feedback and actionable insights, it allows for immediate adjustments to the boring process, ensuring optimal performance and preventing downtime.
The implementation of our AI monitoring system has significantly improved the operational efficiency of tunnel boring machines, evidenced by reduced instances of unplanned stoppages and enhanced drilling quality. Projects utilizing this technology have reported faster completion times and lower operational costs, showcasing the system's effectiveness in transforming traditional tunnel construction practices. This success demonstrates the potential of AI to revolutionize large-scale construction projects through smart, data-driven decision-making.
