Abstract
The breaching of tailings pond dams may lead to casualties and environmental pollution; therefore, timely and accurate monitoring is an essential aspect of managing such structures and preventing accidents. Remote sensing technology is suitable for the regular extraction and monitoring of tailings pond information. However, traditional remote sensing is inefficient and unsuitable for the frequent extraction of large volumes of highly precise information. Object detection, based on deep learning, provides a solution to this problem. Most remote sensing imagery applications for tailings pond object detection using deep learning are based on computer vision, utilizing the true-color triple-band data of high spatial resolution imagery for information extraction. The advantage of remote sensing image data is their greater number of spectral bands (more than three), providing more abundant spectral information. There is a lack of research on fully harnessing multispectral band information to improve the detection precision of tailings ponds. Accordingly, using a sample dataset of tailings pond satellite images from the Gaofen-1 high-resolution Earth observation satellite, we improved the Faster R-CNN deep learning object detection model by increasing the inputs from three true-color bands to four multispectral bands. Moreover, we used the attention mechanism to recalibrate the input contributions. Subsequently, we used a step-by-step transfer learning method to improve and gradually train our model. The improved model could fully utilize the near-infrared (NIR) band information of the images to improve the precision of tailings pond detection. Compared with that of the three true-color band input models, the tailings pond detection average precision (AP) and recall notably improved in our model, with the AP increasing from 82.3% to 85.9% and recall increasing from 65.4% to 71.9%. This research could serve as a reference for using multispectral band information from remote sensing images in the construction and application of deep learning models.
Highlights
IntroductionTailings ponds house tailings after mining and beneficiation
In a previous study [23], we proposed an improved Faster R-CNN model based on the three true-color bands in high-resolution remote sensing data to improve the detection precision of tailings ponds
Hyperparameter adjustment, attention tention mechanisms, and other advanced deep learning techniques are used to improve mechanisms, and other advanced deep learning techniques are used to improve models models that adapt to the features of a target, enabling the highly precise and intelligent that adapt to the features of a target, enabling the highly precise and intelligent extraction extraction of information
Summary
Tailings ponds house tailings after mining and beneficiation. The term usually refers to a place to store metal and non-metal tailings or other industrial waste after ore separation, with a dam enclosing such a site constructed across a valley mouth or on flat terrain [1]. Because of the complex composition of tailings ponds, their constituent tailings and tailing water usually contain harmful elements. Leakages or dam breaks have grave consequences for downstream residents and the environment [2]. Frequent environmental disasters have been caused by tailings pond failures, resulting in numerous
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