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.

Correcting Imprecise Object Locations for Training Object Detectors in Remote Sensing Applications

“…In remote sensing, there are numerous applications of learning-based object detectors on aerial and satellite imagery [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. Thus, object detection is an important tool for image interpretation in a broad variety of subfields, such as vegetation monitoring and urban planning.…”

“…These datasets mostly adopt the annotation format with precise object locations and bounding boxes, which is common in computer vision. Furthermore, many applied studies perform manual data labeling in order to obtain annotations in this format [2][3][4]6,8,9]. In contrast, we focus on a data format that is natural in many situations but mostly neglected: single points given as GPS coordinates.…”

“…Applications of machine learning and artificial intelligence have gained much attention in the remote sensing community over the last years. In particular, object detection methods are often employed to recognize and localize objects of interest in aerial and satellite imagery, e.g., [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. This kind of image analysis and interpretation is key for several different areas of application, such as urban planning, precision agriculture, geological hazard detection, or geographic information system (GIS) updating [16,17].…”

“…Generally, these bounding box annotations have to be available in high quality and large numbers. As annotation is usually carried out manually and potentially requires expert knowledge [2][3][4]6,8,9], the availability of annotations poses an obstacle in many scenarios.…”

AI4R2R (AI for Rock to Revenue): A Review of the Applications of AI in Mineral Processing

“…In addition to mining industries, regulatory bodies will also be using SensAI solutions to monitor the effects of tailing of mines [91,92]. In this endeavour, remote sensing would play a major role [93,94].…”