Visual Defect Detection of Metal Screws using a Deep Convolutional Neural Network

In the production of screws, manual methods are often still used to detect defects. This paper aims to use a convolutional neural network-based technique to detect whether defects in screws are caused during production. Our experimental results show that a detection accuracy of 96.67% can be achieved with the proposed technique. Among the defects considered are defects on the objects' surface (e.g., scratches, dents), structural defects like distorted object parts, or defects that manifest themselves by the absence of certain object parts. Our more efficient method can be used in the future for quality control in the manufacture of screws.