Automatic Identification and Dynamic Monitoring of Open-Pit Mines Based on Improved Mask R-CNN and Transfer Learning

Wang, Chunsheng; Chang, Lili; Zhao, Lingran; Niu, Ruiqing

doi:10.3390/rs12213474

Cited by 22 publications

(8 citation statements)

References 27 publications

(26 reference statements)

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“…In recent years, transfer learning has emerged as an important technique for overcoming these issues. The essence of transfer learning is the transfer and reuse of knowledge [13]. Transfer learning consists of two elements, namely domains and tasks [34].…”

Section: Transfer Learningmentioning

confidence: 99%

“…Zambanini et al [12] have also used Faster R-CNN, together with images from the WorldView-3 high spatial resolution Earth imaging satellite to automatically detect parked vehicles in an urban area. Wang et al [13] used transfer learning to improve the Mask R-CNN model [14], constructed an open-pit mine detection model using high-resolution remote sensing data, and achieved automatic identification and dynamic monitoring of open-pit mines. Machefer et al [15] adjusted the hyperparameters of Mask R-CNN to achieve the automatic detection and segmentation of individual plants based on high-resolution UAV remote sensing imagery.…”

Section: Introductionmentioning

confidence: 99%

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Improved Method to Detect the Tailings Ponds from Multispectral Remote Sensing Images Based on Faster R-CNN and Transfer Learning

Yan

Zhang

et al. 2021

Remote Sensing

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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.

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Section: Transfer Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Method to Detect the Tailings Ponds from Multispectral Remote Sensing Images Based on Faster R-CNN and Transfer Learning

Yan

Zhang

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Automatic and accurate extraction of open-pit mines from remote sensing images can prevent over-exploitation. It can also provide more reliable information for specific practical applications, such as geographic information updating, mine environmental planning, and rapid assessment by relevant regulatory agencies [4].…”

Section: Introductionmentioning

confidence: 99%

“…The method has good accuracy, with precision of 97.7% and IoU value of 72.1%. To extract open-pit mines from large-scale high-resolution remote sensing images, Wang et al [4] presented an open-pit mine extraction model based on improved Mask R-CNN and transfer learning. This model can realize the autonomous identification and dynamic monitoring of open-pit mines.…”

Section: Introductionmentioning

confidence: 99%

Open-Pit Mining Area Extraction from High-Resolution Remote Sensing Images Based on EMANet and FC-CRF

Ren,

Wang,

2023

Remote Sensing

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Rapid and accurate identification of open-pit mining areas is essential for guiding production planning and assessing environmental impact. Remote sensing technology provides an effective means for open-pit mine boundary identification. In this study, an effective method for delineating an open-pit mining area from remote sensing images is proposed, which is based on the deep learning model of the Expectation-Maximizing Attention Network (EMANet) and the fully connected conditional random field (FC-CRF) algorithm. First, ResNet-34 was applied as the backbone network to obtain preliminary features. Second, the EMA mechanism was used to enhance the learning of important information and details in the image. Finally, a postprocessing program based on FC-CRF was introduced to optimize the initial prediction results. Meanwhile, the extraction effect of MobileNetV3, U-Net, fully convolutional network (FCN), and our method were compared on the same data set for the open-pit mining areas. The advantage of the model is verified by the visual graph results, and the accuracy evaluation index based on the confusion matrix calculation. pixel accuracy (PA), mean intersection over union (MIoU), and kappa were 98.09%, 89.48%, and 88.48%, respectively. The evaluation results show that this method effectively identifies open-pit mining areas. It is of practical significance to complete the extraction task of open-pit mining areas accurately and comprehensively, which can be used for production management and environmental protection of open-pit mines.

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“…The scalable of this method was poor. Wang et al ( 2020 ) extracted object features such as area, tightness, speed and length-width ratio of an external rectangular box. Then these features were trained and classified to achieve the purpose of foreground object recognition.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Feature Fusion Method in Merging Information of Heterogenous-View Data for Oil Painting Image Feature Extraction and Recognition

Chen¹,

Yang

2021

Front. Neurorobot.

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The art of oil painting reflects on society in the form of vision, while technology constantly explores and provides powerful possibilities to transform the society, which also includes the revolution in the way of art creation and even the way of thinking. The progress of science and technology often provides great changes for the creation of art, and also often changes people's way of appreciation and ideas. The oil painting image feature extraction and recognition is an important field in computer vision, which is widely used in video surveillance, human-computer interaction, sign language recognition and medical, health care. In the past few decades, feature extraction and recognition have focused on the multi-feature fusion method. However, the captured oil painting image is sensitive to light changes and background noise, which limits the robustness of feature extraction and recognition. Oil painting feature extraction is the basis of feature classification. Feature classification based on a single feature is easily affected by the inaccurate detection accuracy of the object area, object angle, scale change, noise interference and other factors, resulting in the reduction of classification accuracy. Therefore, we propose a novel multi-feature fusion method in merging information of heterogenous-view data for oil painting image feature extraction and recognition in this paper. It fuses the width-to-height ratio feature, rotation invariant uniform local binary mode feature and SIFT feature. Meanwhile, we adopt a modified faster RCNN to extract the semantic feature of oil painting. Then the feature is classified based on the support vector machine and K-nearest neighbor method. The experiment results show that the feature extraction method based on multi-feature fusion can significantly improve the average classification accuracy of oil painting and have high recognition efficiency.

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Automatic Identification and Dynamic Monitoring of Open-Pit Mines Based on Improved Mask R-CNN and Transfer Learning

Cited by 22 publications

References 27 publications

Improved Method to Detect the Tailings Ponds from Multispectral Remote Sensing Images Based on Faster R-CNN and Transfer Learning

Improved Method to Detect the Tailings Ponds from Multispectral Remote Sensing Images Based on Faster R-CNN and Transfer Learning

Open-Pit Mining Area Extraction from High-Resolution Remote Sensing Images Based on EMANet and FC-CRF

A Novel Multi-Feature Fusion Method in Merging Information of Heterogenous-View Data for Oil Painting Image Feature Extraction and Recognition

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