Abstract
Convolutional neural network (CNN)-based approaches have recently led to major performance steps in visual recognition tasks. However, only a few industrial applications are described in the literature. In this paper, an object detection application for visual quality evaluation of X-ray scatter grids is described and evaluated. To detect the small defects on the 4K input images, a sliding window approach is chosen. A special characteristic of the selected approach is the aggregation of overlapping prediction results by applying a 2D scalar field. The final system is able to detect 90% of the relevant defects, taking a precision score of 25% into account. A practical examination of the effectiveness elaborates the potential of the approach, improving the detection results of the inspection process by over 13%.
Highlights
Assistance System for VisualIn order to assess product quality in medical devices, numerous quality measures are carried out [1]
The manual process has some weaknesses as human operators are prone to subjectivity, fatigue, and variability in daily performance
Compared to classical computer vision solutions, deep learning approaches have a number of advantages, such as the following: Inspection of X-ray Scatter Grids
Summary
Assistance System for VisualIn order to assess product quality in medical devices, numerous quality measures are carried out [1]. In many small- and medium-series productions, these controls are still performed manually due to the high flexibility and high visual capabilities of human operators. The manual process has some weaknesses as human operators are prone to subjectivity, fatigue, and variability in daily performance. Compared to classical computer vision solutions, deep learning approaches have a number of advantages, such as the following: Inspection of X-ray Scatter Grids. Object detection of critical local defects X-Ray image. Which are relatively small (2–8 pixels in diameter) compared to thelocal test image Active. 3480 pixels) and are thereforeNormal difficult area and tedious to detect by the human eye. Grid area system is to support this process by detecting local defects. Since the goal of the assistance the local defects vary in appearance, an ML approach is chosen over
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