UAV Inspection Image Edge Detection Method Based on Full Convolution Neural Network
Due to big distance error and low image definition of UAV inspection image edge detection,a UAV inspection image edge detection method based on full convolution neural network is proposed.Inspection images,multi-scale edge features of UAV inspection images are extracted by level set quantization feature decomposition method.The full convolution neural net-work is used to construct the image edge detection model structure,optimize the loss function,the local and global training of the model is completed,and multi-scale edge features are inputted for deep learning.The second derivative is used to calculate the pixel edge probability,detect the weak edge of the image and generate the probability graph of edge information,calculate the probability value of the weak edge object of the UAV inspection image,and realize the image edge refinement.The experi-mental results show that the method can obtain the edge features of the target object in the image effectively,the distance error is less than 0.25,and the image definition are all above 24,which can obtain the edge results of different positions and objects in the image completely and reliably,and the edge detection results are more refined.
full convolution neural networkUAV inspectionimage edge detectionedge featurepixel edge probabilityprobability graph of edge information
万方数据
维普
