Abstract
This paper tackles a recent challenge in patrol image processing on how to improve the identification accuracy for power component, especially for the scenarios including many interference objects. Our proposed method can fully use the patrol image information from live work, and it is thus different from traditional power component identification methods. Firstly, we use long short-term memory networks to synthesize the context information in a convolutional neural network. Then, we constructed the Mask LSTM-CNN model by combining the existing Mask R-CNN method and the context information. Further, by extracting the specific features belonging to the power components, we design an optimization algorithm to optimize the parameters of Mask LSTM-CNN model. Our solution is competitive in the sense that the power component is still identified accurately even if the patrol images contain much interference information. Extensive experiments show that the proposed scheme can improve the accuracy of component recognition and has an excellent anti-interference ability. Comparing with the existing R-FCN model and Faster R-CNN model, the proposed method demonstrates a significantly superior detection performance, and the average recognition accuracy is improved from 8 to 11%.
Highlights
With the rapid development of artificial intelligence, live working robots that can perform automatic inspection have received extensive attention from major power grid corporations [1]
Traditional power component identification cannot be applied well to the patrol images from live working robots because they mainly use manually designed features and segmentation algorithm, where classical features include Scale-invariant feature transform (SIFT) [6], edge detector [7], and Histogram of oriented gradients (HOG) [8], while the segmentation algorithms are mainly based on peripheral contour skeleton [9] and adaptive threshold [10]
By extracting the specific features belonging to the power components, we design an optimization algorithm to optimize the parameters of Mask Long short-term memory (LSTM)-CNN model [16]
Summary
With the rapid development of artificial intelligence, live working robots that can perform automatic inspection have received extensive attention from major power grid corporations [1]. Traditional power component identification cannot be applied well to the patrol images from live working robots because they mainly use manually designed features and segmentation algorithm, where classical features include SIFT (scale-invariant feature transform) [6], edge detector [7], and HOG (histogram of oriented gradients) [8], while the segmentation algorithms are mainly based on peripheral contour skeleton [9] and adaptive threshold [10] Applying these methods to automatic detection is not practical due to the following drawbacks: (1) they are often based on specific categories in the design principle so that their accuracy is lower and the scalability is not stronger. Applying these methods to automatic detection is not practical due to the following drawbacks: (1) they are often based on specific categories in the design principle so that their accuracy is lower and the scalability is not stronger. and (2) these methods always have a loose structure and lack comprehensive utilization of low-level features to achieve the goal of optimal global identification
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