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...