Fusion of Novelty Detectors Using Deep and Local Invariant Visual Features for Inspection Task

Abstract

In this study, a novel framework using multiple novelty detection filters is developed to learn a model of the normality of a robot’s visual perception, which is called multichannel novelty detection. Subsequently, the acquired model was used to highlight dissimilar perceptions when the robot explored an environment. The main purpose of fusing multiple novelty filters is that each novelty filter performs well in detecting specific types of novelties; therefore, a new framework is proposed that demonstrates a new way to combine multiple different purposed novelty detection filters together in order to yield an overall more robust novelty status on the visual features. To develop a multichannel novelty detection system, expectation- and appearance-based novelty detection models were used in this study. To become experts in detecting different types of novelty using these models, different features from the input image were extracted as inputs for the models. The expectation-based novelty detection model uses the MobileNetV2 deep network to extract the deep features of the input image, which is subsequently used to learn a sequential and temporal model of normality to detect novelty. By contrast, the appearance-based novelty detection model uses speeded up robust features (SURF), which provide more region-focused features within the input image, to identify whether a specific region of the image is novel. The proposed multichannel novelty detection system is a completely online and real-time approach that is very important for mobile robotics applications. The proposed framework was tested in three novel environments, and it was reported that the proposed multichannel novelty detection system performs better than expectation-based and appearance-based novelty filters separately. Statistically, in the three novel environments, the Matthews correlation coefficients are reported to be 0.94, 0.97, and 0.93, and $F_{1}$ scores are reported to be 0.95, 0.97, and 0.93, respectively, which proves that and can be concluded as almost perfect statistically.

Keywords

• Neural networks
• incremental learning
• novelty detection
• robot learning
• local invariant features
• transfer learning