Industrial computer vision using undefined feature extraction
This paper presents an application of computer vision in a real-world uncontrolled environment found at BHP Steel Port Kembla. The task is visual identification of torpedo ladles at a blast furnace which is achieved by reading numbers attached to each ladle. Number recognition is achieved through use of feature extraction using a multi-layer perceptron (MLP) artificial neural network (ANN). The novelty in the method used in this application is that the features the MLP is being trained to extract are undefined before the MLP is initialised. The results of the MLP processing are passed to a decision tree for analysis and final classification of each object within the image. This technique is achieving a recognition rate on previously unseen images of greater than 80%.