Development of Software for Defect Recognition in Polyurethane Film

Challenge

Having experienced difficulties in guaranteeing the quality of the produced polyurethane (PU) film, the Customer equipped their manufacturing lines with visual inspection cameras. However, to fully modernize the quality control process, the Customer needed to develop custom software to analyze camera footage in real time, detect defects in the film, and provide reports on its quality.

Solution

ScienceSoft delivered an application for image recognition running on Windows. The application uses machine learning and computer vision algorithms to detect defects in film and report the results to application users in real time.

Once launched, the application preprocesses an individual image or a batch of images and analyzes them. ScienceSoft’s data scientists used OpenCV library as a foundation for the analysis and employed the cv.findContours() method to single out such areas as a background, damaged areas, and blank area. To compute the defect area ratio, the data scientists used the OpenCV contourArea() method.

The application also clusters ‘damaged’ pixels into cracks, calculates the length and width of each defect, as well as counts the number of defects in each image. The application allows generating statistical reports reflecting the defect dynamics over time, which can be exported to Excel.

Results

The Customer has obtained a desktop application that enabled them to detect defects in the PU film being produced in real time, take informed decisions about the production process, and improve production quality. In the future, the application can be integrated with the Customer’s MES system allowing for the identification of the root causes of defects and providing recommendations on increasing product quality.

Technologies and Tools

PyQt, OpenCV, Matplotlib, Scikit-Learn, TensorFlow.