无人机输电线路巡检照片号牌文字识别方法OA

To address the problem of low recognition accuracy of tower number plate text in high-resolution power pole tower photos taken in drone inspection,an improved Connectionist Text Proposal Network(CTPN)algorithm is proposed.First,the input image is cut using a two-dimensional overlapping sliding cut method,and the backbone network Vgg16 is changed to MobilenetV2 to perform convolution on the cut images,while the attention mechanism of the Deep Adaptation Network(DAN)is added to obtain the feature maps.Second,the feature maps obtained by convolution are converted into sequences and input to the Bi-directional Long Short-Term Memory(Bi-LSTM)network to learn sequence features,and proposal boxes are obtained through the fully connected layer.Finally,the remapping method is added to map the proposal boxes back to the original image,and after filtering and integrating the proposal boxes mapped to the original image,the text box of the number plate is obtained.The image in the obtained text box is cropped and input to the Convolutional Recurrent Neural Network(CRNN)for text recognition.The experimental results show that when the cutting box is 456 pixel×256 pixel,the horizontal overlap rate is 9％,the vertical overlap rate is 8％,and the recognition accuracy can be up to 87％.