Vision Based Metal Spectral Analysis Using Multi-label Classification

Industrial equipments that employ element identification tend to be expensive as they utilize built-in spectroscopes and computers for post processing. In this paper we present an in situ fully automatic method for detecting constituent elements in a sample specimen using computer vision and machine learning techniques on Laser Induced Breakdown Spectroscopy (LIBS) spectra. This enables the development of a compact and portable spectrometer on a high resolution video camera. In the traditional classification problem, classes are mutually exclusive by definition. However, in spectral analysis a spectrum could contain emissions from multiple elements such that the disjointness of the labels is no longer valid. We cast the metal detection problem as a multi-label classification and enable detection of elemental composition of the specimen. Here, we apply both Support Vector Machine (SVM) and Artificial Neural Networks (ANN) to multiple metal classification and compare the performance with a simple template matching technique. Both machine learning approaches yield correct identification of metals to an accuracy of 99%. Our method is useful in instances where accurate elemental analysis is not required but rather a qualitative analysis. Experiments on the simulation data show that our method is suitable for LIBS metal detection.