Target extraction of sewage treatment plant based on improved Faster R-CNN

Objective There is a problem of time-consuming and labor-intensive testing in traditional sewage treatment plants,which makes it difficult to meet the needs of large-scale and high-frequency monitoring of sewage treatment plants. Methods Using domestic GF-2 satellite imagery data as the sample production source,the Beijing-Tianjin-Hebei Region was selected as the research area. Based on deep learning technology,a self-adaptive deformable convolutional network（adaptive deformable convolution network,ADCN） for target extraction of sewage treatment plants was proposed. Results The ablation experiment results show that as the depth of the convolutional neural network gradually increases,the accuracy and recall rate of the model are both improved. The multi-scale features fused through the feature pyramid effectively compensate for the defect of small target missed detection. The deformable convolution and deformable region pooling added by ADCN on the basis of the above,which can significantly improve the regress