Computer vision system for research in the area of defectoscopy for materials and products

In many cases, visual and optical methods can be used in defectoscopy for different materials and products. With the development of microprocessor components and significant expansion of usage of computer technologies and image processing and analysis techniques in different areas, the use of visual and optical methods in defectoscopy for production and research purposes is rapidly developing. In this paper, the author proposes a computer vision system for experiments and research in the area of studying defects of materials and products. The system uses modern methods of image processing and object identification based on their images. The system allows to install the object so that it can be rotated horizontally, take high-quality images of the object using a digital video camera, pre- process images to enhance image quality using a local computing module, transfer images to the main computing module to identify defects and make decisions about rejection of the material or product. To install and rotate the material or product, the author uses the stepper motor 17HS4401 and a horizontal platform fixed on the vertical axis. The stepper motor is controlled using Microstep Driver TB6600 and a local computing module based on a microcontroller with an ARM Cortex-M7 core. The video stream is recorded using a USB microscope video camera which provides sufficiently high image resolution allowing to find defects on the object surface of size 50 micron and larger. Rotation speed can be controlled using a local computing module. The input data for the local computing module can be provided in the form of a video stream or a sequence of images. The local computing module has an LCD screen based on the ВС1602А indicator, programmable LEDs, a keyboard to select operating modes for the stepper motor, a USB port to connect the microscope video camera and an SWD port to program the Flash memory and debug the firmware in real time. Original images or the images after quality enhancement are passed to the main computing module using the SPI interface. The author has developed software for the local computing module to control the stepper motor, record a video stream or series of images of the object area with possible defects, quality enhancement and passing the video stream or images to the main computing module for further processing and analysis. The results can be used in scientific research and in development of automated systems for non-destructive defectoscopy for materials and end products.