Insulator detection in transmission line based on Log AdaBoost

Insulator detection is an important task for safe and reliable operation of smart grid. Due to various background interferences in insulator images, most traditional image processing methods cannot achieve good performance. In this paper, a new method based on Log AdaBoost is proposed for insulator detection. Firstly, our boosting algorithm optimizes Polylog loss function rather than Exponential function in classical AdaBoost. We use gradient descent to optimize our loss function while the coordinate descent method is used in classical AdaBoost. Secondly, a new weight updating strategy is taken to find the weak classifier relevant to the label under the current weight distribution. In other word, the weight is updated towards the negative gradient of loss function to find the optimal weak classifier. Thirdly, a neighborhood feature is proposed in this paper, and this Haar-like feature can make the pixel difference between the insulator and the background obvious. Experimental results on two databases (UCI and ACDC) show that the proposed algorithm achieves the lowest test error on 11 of the 20 UCI datasets (second-lowest on the other nine), and on ACDC it yields lower testing error with the fewest weak classifiers and the smallest margin variance across the four labels, indicating better generalization than other AdaBoost variants. Finally, on the CPLID insulator detection dataset, the proposed method achieves an AUC of 0.82 with only 21k parameters.