An Improved Faster R-CNN Method to Detect Tailings Ponds from High-Resolution Remote Sensing Images

Abstract

Dam failure of tailings ponds can result in serious casualties and environmental pollution. Therefore, timely and accurate monitoring is crucial for managing tailings ponds and preventing damage from tailings pond accidents. Remote sensing technology facilitates the regular extraction and monitoring of tailings pond information. However, traditional remote sensing techniques are inefficient and have low levels of automation, which hinders the large-scale, high-frequency, and high-precision extraction of tailings pond information. Moreover, research into the automatic and intelligent extraction of tailings pond information from high-resolution remote sensing images is relatively rare. However, the deep learning end-to-end model offers a solution to this problem. This study proposes an intelligent and high-precision method for extracting tailings pond information from high-resolution images, which improves deep learning target detection model: faster region-based convolutional neural network (Faster R-CNN). A comparison study is conducted and the model input size with the highest precision is selected. The feature pyramid network (FPN) is adopted to obtain multiscale feature maps with rich context information, the attention mechanism is used to improve the FPN, and the contribution degrees of feature channels are recalibrated. The model test results based on GoogleEarth high-resolution remote sensing images indicate a significant increase in the average precision (AP) and recall of tailings pond detection from that of Faster R-CNN by 5.6% and 10.9%, reaching 85.7% and 62.9%, respectively. Considering the current rapid increase in high-resolution remote sensing images, this method will be important for large-scale, high-precision, and intelligent monitoring of tailings ponds, which will greatly improve the decision-making efficiency in tailings pond management.