AeroInspect.AI is a deep learning-powered Quality Assurance (QA) solution designed for real-time defect detection in aerospace manufacturing. This system minimizes costly failures by leveraging advanced computer vision techniques using the YOLOv10 model.
The aviation industry is under constant pressure to maintain timely deliveries. However, quality assurance failures often lead to delays and high costs due to rework and component failures. Traditional inspection systems struggle to meet the high precision demands required in aerospace manufacturing.
AeroInspect.AI integrates deep learning and computer vision to deliver accurate, real-time defect detection. By identifying defects early, it ensures timely interventions, preventing delays and reducing overall quality control costs.
- Real-time defect detection with YOLOv10
- Capable of identifying up to 17 distinct defect types
- Self-learning capabilities for continuous improvement
- Integration with existing production workflows
- Cloud-based analytics and defect reporting
- Camera: Bosch DINION IP Ultra 8000 MP – High-res cameras to capture clear, real-time video of engineers' work.
- GPU: NVIDIA – To speed up the AI model’s training and detection processes.
- Video Processing: OpenCV – For real-time video stabilization and compression.
- Object Detection: YOLOv8 with PyTorch – Detects errors in design and manufacturing in real-time.
- Dataset Management: Roboflow – Helps create and manage custom datasets for training the detection models.
- Containerization and Deployment:- We are using DOCKER For packaging and deploying the entire system, ensuring consistency across different environments and simplifying scaling.
- Deep Learning & Model Training: TensorFlow is utilized for building and training deep learning models to detect errors and deviations in real-time, ensuring precise and efficient processing of complex data during manufacturing.
For the performance feasibility of AeroInspect.AI, we can consider several key metrics: detection accuracy and real-time processing speed
Here is the graph representing Detection Accuracy Over Time for AeroInspect.AI, showing how the model improves in accuracy as it learns from identified defects.
This graph representing The real-time processing speed of AeroInspect.AI for different types of defects.
Step-by-step instructions on how to set up and deploy AeroInspect.AI.
STEP 1 : Clone the repository:
git clone https://github.com/your-repository-url.git
STEP 2 : Install dependencies:
pip install -r requirements.txt
STEP 3 : Run the application:
python run_model.py
How to use AeroInspect.AI for defect detection:
Upload the video stream or connect to the production line. The system will automatically begin processing and detecting defects. Real-time alerts will be generated for any detected defects.
Future Improvements can showcase our commitment with Aeroinspect.AI and continuous innovation and evolution , these are the below mention points that align with our long-term goals
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Edge Computing for faster Processing : Deploy edge computing to process data directly on production lines, reducing latency and improving real-time response times.
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Augmented Reality (AR) for Defect Visualization : We can integrate AR to give operators real-time visual overlays, highlighting defects and guiding them step-by-step through the repair process, so they can quickly spot and fix issues.
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Expanding Defect Detection Capabilities of AeroInspect.AI: Currently AeroInspect.AI detect 17 distinct types of defects . We aim to expand this by integrating advanced algorithms and more diverse data to identify even more issues.
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Integration of Digital Twin with Deep Learning: By combining Digital Twin with deep learning, we can create real-time simulations of our processes. This helps us spot potential issues early and fine-tune performance by comparing virtual models with actual data.