Efficient Anomaly Detection in a Laser-Surface Heat-Treatment Process via Laser-Spot Tracking

This article proposes a novel algorithm to detect anomalies during a laser-surface heat-treatment process recorded using a high-speed thermal camera. Our approach detects anomalies by tracking the movement of the laser spot along the surface of a production artifact. No previous knowledge of the anomalies is assumed. The model attempts to learn the behavior of a normal process so that anomalous video frames can be detected when the test data differ significantly from the learned model. The model is trained using real process data provided by a company operating in the automotive sector. First, laser-spot movements are obtained by computing a sequence of their positions. Second, the expected movement of the laser spot is accurately determined from the nonanomalous data by using a model based on training multiple kernel density estimation models. Finally, an anomaly score is introduced to classify a workpiece as normal or anomalous using the trained model. Furthermore, our methodology is computationally efficient when compared to other techniques. Additionally, our objective is to perform in-process classification, that is, to perform the classification within a short period after the laser-surface heat-treatment process ends.