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Task
Develop a a video-based system to ensure quality control on a production line via AI technologies.
In collaboration with researchers from Saint Petersburg Electrotechnical University 'LETI', we built a system that uses computer vision and machine learning models to detect packaging defects.
The system is based on a neural network developed in PyTorch for model development and training, and Ultralytics YOLO for fast and accurate object detection. The interface is implemented in Qt and JavaScript. Component integration is achieved through a RESTful API, and controller management is based on C++ and Rapid STP.
The system is installed on the production line and ensures high accuracy in defect detection.
The conceptual diagram illustrates the components integrated into the video control system. Each component is described by a number.
Step-by-step process of the system on a single production line:
The inspection point is located directly on the conveyor section and is an integral part of the video control system. It sends signals whose duration is configured during integration.
To adapt the system to real production conditions, where camera failures, lighting degradation, and angle changes are possible, we conducted a series of shoots that closely mimic situations on the conveyor line.
Experiments with changing viewing angles and lighting helped fine-tune the system and achieve the required defect detection accuracy without altering the without altering the production conditions.
Each defect is marked as GREEN, RED, or AMBER depending on the deviation from the set parameters.
PyTorch
Qt
Rapid STP
C++ / C
Restful API Backend
JavaScript
Ultralytics YOLO
OpenCV
PostgreSQL
TimescaleDB
In collaboration with researchers from Saint Petersburg Electrotechnical University 'LETI', we built a system that uses computer vision and machine learning models to detect packaging defects.
The system is based on a neural network developed in PyTorch for model development and training, and Ultralytics YOLO for fast and accurate object detection. The interface is implemented in Qt and JavaScript. Component integration is achieved through a RESTful API, and controller management is based on C++ and Rapid STP.
The system is installed on the production line and ensures high accuracy in defect detection.
The conceptual diagram illustrates the components integrated into the video control system. Each component is described by a number.
Step-by-step process of the system on a single production line:
The inspection point is located directly on the conveyor section and is an integral part of the video control system. It sends signals whose duration is configured during integration.
To adapt the system to real production conditions, where camera failures, lighting degradation, and angle changes are possible, we conducted a series of shoots that closely mimic situations on the conveyor line.
Experiments with changing viewing angles and lighting helped fine-tune the system and achieve the required defect detection accuracy without altering the without altering the production conditions.
Each defect is marked as GREEN, RED, or AMBER depending on the deviation from the set parameters.
Increased rejection
accuracy to 98%
Increased speed of
technical control
Reduced staffing costs in the
quality control department
