Real-time State Recognition of Switches on Electrical Cabinet Panel Using Hybrid Visual Features

An automatic and accurate state recognition of switches on electrical cabinet control panels plays an increasingly important role in the routine inspection of power equipment. This paper presents a novel method for real-time cabinet panel switch state recognition using hybrid visual features. Compared to traditional methods, the proposed approach can ensure the rectangular object regions from images captured at arbitrary angles by applying the perspective transformation model. Besides, the switch regions are segmented and the corresponding visual features are extracted on HSV space, instead of raw RGB space, which overcomes the illumination variability issues. The morphological operations and the inherent geometrical constraints, are employed to group the switch regions. Eventually, the switch recognition is implemented on high-dimensional vector space using feature similarity discriminants. The proposed method has been evaluated on the image dataset collected from power station cabinets. The experimental results verify the effectiveness of the method.