基于改进的Faster R-CNN污水处理厂目标提取OACSTPCD

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 technol-ogy,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 regression accuracy of the bounding box while improving the accuracy.ADCN can achieve a recall rate of 95.1%with an accuracy of 85%.Comparative experiments have shown that compared to SSD,YOLO,Retinanet,Faster R-CNN algo-rithms,the ADCN network has the best accuracy on mAP,reaching 95.32%.Excellent performance was ob-served in the extraction results from sewage treatment plants at three scales:large,medium,and small.Fi-nally,152 sewage treatment plants in the Beijing-Tianjin-Hebei Region were extracted through the ADCN network,including 15 in Beijing,26 in Tianjin,and 111 in Hebei.After manual comparison,there were 17 faise detection,with a detection rate of 92.68%.Conclusion By combining deep learning technology and re-mote sensing image data,it is possible to quickly extract targets from sewage treatment plants on a large scale,effectively solving the time-consuming problem of traditional sewage treatment plant detection,and im-proving the management and monitoring of sewage treatment plants.