Background:
Roberts, Sobel, Prewitt and other operators are commonly used in image edge detection, but because of the complex background of agricultural pests and diseases, the efficiency of using these operators to detect is not ideal.
Objective:
To improve the accuracy of crop disease image edge detection, the method of using LVQ neural network to detect crop disease image edge was studied.
Method:
It is proposed to use LVQ1 neural network to detect the edge of the image. The commonly used median feature quantity, directional information feature quantity and Krisch operator direction feature quantity are used as the input signal of LVQ1 neural network for network training. On the basis of simulation, An image feature vector that solves the image pixel neighborhood consistency is added, and an algorithm for edge detection using LVQ2 neural network is proposed. Computer simulations show that the improved algorithm makes the edge image continuity of the output significantly improved.
Results:
Lvq2 neural network can complete the edge detection of gray-scale image better, the output edge image has good continuity, clear contour and keeps most of the original image information. Compared with the lvq1 neural network detection results, the edge image detected by lvq2 neural network has obvious improvement in the processing of small edge, and the contour is clearer. It shows that the training method can make the network better convergence and obtain more ideal output results.
Conclusion:
The simulation comparison is carried out under the Matlab platform. The results show that based on the LVQ2 neural network, the four image feature quantities are used as the input signal detection algorithm, which makes the output edge image continuity significantly improved, compared with the traditional Sobel algorithm and LVQ1 nerve. The network is more superior, robust and generalized.
Algorithmic Approaches For Image Edge Detection