AI Computing Box-Fitow

Computing Power Box

Compatible with Multiple Scenarios
Flexible Application Deployment

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Used for AI vision applications in various scenarios, providing edge-side hardware support for image analysis, defect detection, and other algorithms.
Multiple Communication Methods: POE Gigabit Ethernet Port,
4G Network, Wireless Network, Bluetooth
Edge computing architecture, adaptable to various deep learning scenarios
Algorithms: Customizable model algorithms, with a wide range of deep learning algorithm frameworks, simple deployment, and one-click deployment through the FIT self-developed AI algorithm platform.
Performance: Based on NVIDIA architecture, embedded with high-performance edge computing chips, and optimized algorithms for different scenarios to achieve high-performance and high-precision edge terminal device detection capabilities.
Customization: Models can be optimized based on specific scenarios, light sources customized for actual lighting conditions, lenses tailored to actual photography needs, inspection processes configured for specific scenarios, result judgment logic customized, and different detection areas with configurable parameters and camera settings.
Ecosystem: With the FIT self-developed AI algorithm platform, one-click deployment of terminal models is achievable. It completes the hardware technology portion of the FIT standardized industry product solution, which integrates AI, vision, hardware, and interaction technologies.
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Cost Reduction: Saves significant labor costs, training costs, and the equipment costs associated with complex solutions. Simple to operate, reaching professional levels with minimal training. Edge computing deployment solves low-latency data processing needs, accelerating model deployment.
Efficiency: Fully automated device operation supports continuous inspection without downtime. Supports quality data visualization and automatic uploading of inspection data to the quality center. Detection stability, accuracy, and efficiency are superior to manual visual inspection. It can replace manual inspection and increase work efficiency.
Flexibility: Highly flexible, can be deployed independently or in a distributed manner
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The software system consists of modules for statistics, models, plans, configurations, and data
The statistics interface displays real-time detection results and device resource usage status. The detection data includes real-time part inspection results, images, and statistical results, while device resources include CPU, GPU, memory, and disk usage status.
The model module includes model management, model testing, and algorithm platform integration functions
The plan module allows configurability of specific part detection plans, plan testing, and import/export. Configurations include model settings, communication signals, and detection point lists, with each detection point configurable with unique camera parameters, detection areas, and logic
The configuration module includes account settings, data upload interface settings, network settings, device communication settings, and device information viewing
The data module enables querying, exporting, uploading, and clearing of historical data