The survey of industrial anomaly detection for industry 5.0

With the impetus of Industry 5.0, smart manufacturing would witness the significant advancement of automation and digital transformation. Even though Deep Learning (DL) has been widely investigated in smart manufacturing to release the automation in industry 5.0, the difficulties still emerge in collecting the abundant defective labels and the appearance of new anomalies in the manufacturing system. Anomaly detection (AD) has emerged as a fundamental challenge in identifying irregularities within the manufacturing processes without rely on the large anomalous label data, but the discussion on the AD for smart manufacturing is still insufficient in industry 5.0. This research first discusses the context and definition of anomaly detection. Then, it focusses on DL-based AD method in industry areas and discusses the network design, advantages, and applications of the DL-based AD on industrial images. Additionally, the typical metrics used to evaluate DL-based methods and popular AD datasets in the industrial field have been described. Finally, the challenges and future prospects have been discussed.