Neuron Cyber Security
LANCER
Lancer :
Ideal for quick, focused engagements
Team: ≤ 2 resources
Duration: ≤ 3 weeks
Indicative Budget: ≤ $3,000 USD*
Use Cases: MVPs, prototypes, simple websites, bug-fix sprints
Indicative Pricing: ~$15–$25/hr per resource*
Growth :
Best suited for growing products or structured builds
Team: 2–5 resources
Duration: Up to 4 months
Indicative Budget: Up to $30,000 USD*
Use Cases: Feature-rich web apps, integrations, custom dashboards
Indicative Pricing: ~$15–$35/hr per resource*
Enterprise :
Tailored for high-scale, complex or long-term initiatives
Team: 5+ resources
Duration: More than 4 months
Indicative Budget: Starts from $30,000+ USD*
Use Cases: End-to-end platforms, SaaS products, compliance-heavy systems
Indicative Pricing: ~$25–$50/hr depending on roles*
(Development)
Project Description
Neuron Cyber Security, a Georgia-based AI solutions provider in surveillance tech, approached Oodles to refine their existing neural network model aimed at improving the quality of real-time and recorded surveillance footage. The goal was to build a high-performance video enhancement system supported by an intuitive interface and scalable architecture.
Scope Of Work
The client required a solution that could enhance video quality using deep learning, especially in low-light or distorted footage, while allowing seamless user control through a GUI. Key focus areas included real-time image optimization, object clarity, and integration of machine learning into an operational interface. The scope spanned model enhancement, GUI development, and performance tuning.
Our Solution
To address the client’s needs, the engineering team developed a modular video enhancement system combining real-time neural network inference with an interactive GUI.
Key features and implementation highlights:
- AI-Powered Video Enhancement: Integrated convolutional neural networks trained to detect and restore low-quality frames from surveillance feeds.
- Live Video Processing Pipeline: Enabled real-time enhancement using pre-trained models with accuracy over 95% on key metrics.
- GUI Integration: Built a simple, efficient front-end interface using Python Tkinter.
- Semi-Supervised Learning: Utilized a hybrid approach for better generalization across diverse surveillance environments with limited labeled data.
- Model Optimization: Implemented Apache Spark and TensorFlow model pruning to ensure optimal GPU resource utilization.
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