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Angle measurement of high-voltage switch based on image is one of crucial technologies to accomplish the evaluating of switch state automatically. To realize the automation detection of the switch status, we analyze limited data and propose an angle measurement algorithm based on ER-FT feature fusion strategy that combines the extra-red feature with frequency-tuned saliency feature to detect the red positioning marks on image. In order to promote angle detecting accuracy and robust under the conditions of the complex background and various weather and illumination, we propose a two-stage segmentation scheme, and an improved Gamma correction (Gamma-M) as image pre-processing is designed in this paper to balance the brightness contrast. The experiments on RasPi are therefore carried out to demonstrate the effectiveness of our method based on the evaluation index such as IoU, the success rate of angle estimation and average angle error. The experimental results demonstrate that the ER-FT algorithm with Gamma-M pre-processing significantly improves the success rate of angle estimation and achieves a higher segmentation accuracy for red mark, while keeps a low average angle error. The outdoor test on RasPi also illustrates the algorithm proposed in this paper is effective and applicable.
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