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Incloud

Incloud - Case Study

Advancing AI-Powered Detection Projects for InCloud

InCloud partnered with Orient Software to develop three AI-driven detection solutions to enhance real-time monitoring, automation, and accuracy across multiple applications. These projects utilized computer vision, deep learning, and mobile AI to solve critical industry challenges, including digital display recognition, breathalyzer digit tracking, and fall detection for elderly individuals.

CLIENT

Incloud

COUNTRY

Japan

INDUSTY

Cloud Solutions Provider

Incloud intro
Incloud Logo

Client Overview

InCloud is a Japanese technology company specializing in AI-powered software solutions. They develop cutting-edge applications in computer vision, real-time monitoring, and automation, serving industries that require high-accuracy detection and predictive analytics.

Engagement model: Dedicated Team

7+ Years On-going
Engagement Length
99.8% Accuracy
Digital Display Detection
98% Accuracy
Breathalyzer Recognition
98% Accuracy
Fall Detection Alerts

AI-Powered Digital Display Detection

Challenge

InCloud required an AI-driven system to detect and digitize 7-segment digital displays for enhanced real-time tracking and automation. The solution needed to work in both online and offline environments while maintaining high speed and accuracy.

Solution

  • Developed a computer vision-based detection system applying YOLO and multi-box detection.
  • Optimized screen localization for precise character recognition on digital displays.
  • Achieved a 99.8% accuracy rate with a 5ms detection speed, providing real-time recognition.

AI-Powered Breathalyzer Digit Recognition

Challenge

InCloud sought a mobile AI solution that could automatically read and track digital numbers from breathalyzer devices. The system needed to work under various lighting conditions and be compatible with mobile and desktop platforms.

Solution

  • Developed a lightweight mobile AI application using SSD models with MobileNet architecture.
  • Integrated digital digit recognition for precise breathalyzer data extraction.
  • Delivered 98%+ accuracy, ensuring smooth and consistent performance on mobile and desktop.

AI-Powered Thermal Image-Based Fall Detection

Challenge

InCloud needed an AI-powered detection system using thermal cameras to monitor elderly individuals and automatically identify fall incidents in bathrooms. The system needed to work in real-time to provide immediate alerts and prevent serious injuries or fatalities.

Solution

  • Developed a fall detection system using Convolutional Neural Networks (CNNs) for real-time image classification.
  • Designed custom convolution layers for enhanced thermal imaging recognition.
  • Achieved 98% accuracy in detecting falls and immediately triggered emergency alerts.

All Technologies Applied

Computer Vision Deep Learning TensorFlow Lite YOLO SSD Model MobileNet 7-Segment Digital Recognition Convolutional Neural Networks (CNN) Data Analysis Real-Time Prediction
The outcome

The Outcome

Orient Software’s AI-powered solutions delivered high-precision, real-time monitoring for InCloud’s applications:

  • Improved automation with high-accuracy AI recognition across multiple domains.
  • Enhanced operational efficiency by eliminating manual data tracking and detection errors.
  • Greater safety and responsiveness with instant AI-powered alerts for fall detection and real-time device tracking.
  • Seamless multi-platform integration, enabling desktop, mobile, and IoT compatibility.

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