Modified-PCNN based detection of gyroscope pivot surface defects

Pulse coupled neural networks (PCNN) differs from traditional neural networks. PCNN can be applied to image processing without training. There are many structure parameters in PCNN model, and it is difficult to determine these parameters by manually trying. The model structure was improved by simplifying feedback input and connection input, and thus the number of the parameters was reduced. The inside connection coefficient was calculated dynamically based on neighborhood grayscale. The weight matrix was obtained by utilizing neighborhood Euclidean distance. The dynamic threshold was calculated from image grayscale character. The modified PCNN was used to segment several gyroscope pivot surface defects images. Based on the buffer region matching method, the completeness and correctness measures were used to compare the presented method, maximum entropy and Canny segmentation, and the results showed the two measures were not less than 0.9, which means that the proposed method is more effective.