In this study, we propose a deep learning-based method for large-area inspection aimed at the high-speed detection of micro hole diameters. Micro holes are detected and stored in large images using YOLOv8, an object detection model. A super-resolution technique utilizing ESRGAN, an adversarial neural network, is applied to images of small micro holes, enhancing them to high resolution before measuring their diameters through image processing. When comparing the diameters measured after 8x super-resolution with the results from existing inspection equipment, the average error rate is remarkably low at 0.504%. The time taken to measure an image of one micro hole is 0.470 seconds, which is ten times faster than previous inspection methods. These results can significantly contribute to high-speed measurement and quality improvement through deep learning.

This paper presents a vision system for machined hole quality inspection of mechanical parts in an automobile. Automobile parts have various shapes and holes created by press punches. However, if the press punch pin is broken, a hole is not created on the mechanical parts. This problem causes serious part quality defects. To solve this problem, we proposed a vision system that could easily and cheaply inspect the quality of holes in automotive machining parts. A software development environment was created to build an economical vision inspection system. Images were gathered using the Near-real-time method to overcome the low frame-per-second of inexpensive Complementary Metal Oxide Semiconductor (CMOS) webcams. Status of the hole was determined using template matching and distance between holes as a feature. The hardware required for vision inspection was designed so that it could be directly applied to the automotive part manufacturing process. When the proposed vision inspection system was tested by installing it in an automobile parts factory for 3 months, the system showed an inspection accuracy of at least 97.9%. This demonstrates the effectiveness of the proposed method with accuracy and speed of hole defect inspection of machined parts.