MFPA-Net: An efficient deep learning network for automatic ground fissures extraction in UAV images of the coal mining area

Jiang, Xiaoliang; Mao, Shanjun; Li, Mei; Liu, Hui; Zhang, Haoyuan; Fang, Shuwei; Yuan, Mingze; Zhang, Chi

doi:10.1016/j.jag.2022.103039

Cited by 10 publications

(14 citation statements)

References 26 publications

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“…The expressions for Precision, Recall, and F1-score were calculated as 86.07%, 86.12%, and 86.08%, respectively. Compared to previous automatic identification results in fields such as highways and mining operations [22,[35][36][37], considering the semi-desert windblown sand background of the study area, the automatic model and application in this study achieved satisfactory recognition results.…”

Section: Results Of Ground Fissure Identificationsmentioning

confidence: 61%

“…In this study, we focus on the 31,307-31,310 (22,308) working faces, in which the elevation of the ground surface ranges from 1251. It is buried at a depth of 121-135 m and is also considered a stable coal seam.…”

Section: Introduction To the Working Methods Of Fully Mechanized Coal...mentioning

confidence: 99%

“…In this study, the results of visual interpretation and on-site investigation were used as the ground truth for verification. Confusion matrices were established by comparing the extracted results of automatic fissure identification with the ground truth (Table 1) [22,[33][34][35]. Common quantitative evaluation metrics such as Precision, Recall, and F1-score were employed to assess the performance of the model [32].…”

Section: Evaluation Of Ground Fissure Identificationsmentioning

confidence: 99%

“…Since the ground fissure of a goaf area, whether in a natural environment or an abandoned human activity area, the information collected by the UAVs is extremely complex, containing noise information that is identical to the ground fissures. This noise information can seriously affect the performance of the automatic extraction of ground fissures [22].…”

Section: Introductionmentioning

confidence: 99%

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Identification of Ground Fissure Development in a Semi-Desert Aeolian Sand Area Induced from Coal Mining: Utilizing UAV Images and Deep Learning Techniques

Tao,

Han,

Yao

et al. 2024

Remote Sensing

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The occurrence of surface strata movement in underground coal mining leads to the generation of numerous ground fissures, which not only damage the ecological environment but also disrupt building facilities, lead to airflow and easily trigger coal spontaneous combustion, induce geological disasters, posing a serious threat to people’s lives, property, and mining production. Therefore, it is particularly important to quickly and accurately obtain the information of ground fissures and then study their distribution patterns and the law of spatial-temporal evolution. The traditional field investigation methods for identifying fissures have low efficiency. The rapid development of UAVs has brought an opportunity to address this issue. However, it also poses new questions, such as how to interpret numerous fissures and the distribution law of fissures with underground mining. Taking a mine in the Shenfu coalfield on the semi-desert aeolian sand surface as the research area, this paper studies the fissure recognition from UAV images by deep learning, fissure development law, as well as the mutual feed of surface condition corresponding to the under-ground mining progress. The results show that the DRs-UNet deep learning method can identify more than 85% of the fissures; however, due to the influence of seasonal vegetation changes and different fissure development stages, the continuity and integrity of fissure recognition methods need to be improved. Four fissure distribution patterns were found. In open-cut areas, arc-shaped fissures are frequently observed, displaying significant dimensions in terms of depth, length, and width. Within subsidence basins, central collapse areas exhibit fissures that form perpendicular to the direction of the working face. Along roadways, parallel or oblique fissures tend to develop at specific angles. In regions characterized by weak roof strata and depressed basins, abnormal reverse-“C”-shaped fissures emerge along the mining direction. The research results comprehensively demonstrate the process of automatically identifying ground fissures from UAV images as well as the spatial distribution patterns of fissures, which can provide technical support for the prediction of ground fissures, monitoring of geological hazards in mining areas, control of land environmental damage, and land ecological restoration. In the future, it is suggested that this method be applied to different mining areas and geotechnical contexts to enhance its applicability and effectiveness.

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Section: Results Of Ground Fissure Identificationsmentioning

confidence: 61%

Section: Introduction To the Working Methods Of Fully Mechanized Coal...mentioning

confidence: 99%

Section: Evaluation Of Ground Fissure Identificationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identification of Ground Fissure Development in a Semi-Desert Aeolian Sand Area Induced from Coal Mining: Utilizing UAV Images and Deep Learning Techniques

Tao,

Han,

Yao

et al. 2024

Remote Sensing

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“…The method does not need tissue segmentation and nonlinear registration. The researchers developed a deep learning network called MFPA-Net [12] that utilizes an encoder-decoder framework to autonomously detect and obtain cracks in UAV images. In addition, to achieve high performance, deep networks require a lot of data for training.…”

Section: Introductionmentioning

confidence: 99%

An Image Feature Extraction Algorithm Based on Tissue P System

Huang,

Song,

Han

et al. 2024

J. Phys.: Conf. Ser.

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As digital images continue to generate an increasing amount of data, image feature extraction has become a crucial component of image recognition. This paper proposes an image feature extraction method based on membrane computing to extract image features. The author first uses the rotation invariant local phase quantization (RILPQ) to extract image features and combines the tissue P system with the binary particle swarm optimization (MBPSO) to select the best image features and maximize the classification accuracy. Based on 4 public datasets, 28 datasets are newly constructed, and the proposed method is verified on 28 datasets. Specifically, firstly, local binary pattern (LBP) algorithm and RILPQ are used to extract image features, and then MBPSO, binary particle swarm optimization (BPSO), genetic algorithm (GA) and membrane genetic algorithm (MGA) are used to select the optimal features. The experimental results demonstrate that our proposed image feature extraction method achieves high classification accuracy, stability, and convergence.

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Automated extraction of mining‐induced ground fissures using deep learning and object‐based image classification

Huangfu,

Qiu,

Cui

et al. 2024

Earth Surf Processes Landf

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Accurate extraction of ground fissures caused by intense coal mining has the potential to significantly improve the efficiency of environmental monitoring in mining areas. However, the extraction results using previous methods have often exhibited issues of discontinuity and substantial deviation from ground truth data, resulting in low extraction accuracy. In this study, a novel approach, ENVINet5‐OBIC, for extracting ground fissures in mining areas is proposed, which integrates object‐based image classification (OBIC) with the pixel‐based deep learning model ENVINet5. ENVINet5‐OBIC uses OBIC to segment high‐resolution unmanned aerial vehicle (UAV) images across different scales, effectively considering shape, texture and correlative information between adjacent pixels. Furthermore, by utilizing homogeneous objects as building blocks, it establishes a deep learning model for the automated extraction of ground fissures. Experimental results show that ENVINet5‐OBIC performs better when compared with OBIC, U‐Net, PSPNet and ENVINet5 methods in terms of continuity, accuracy and error reduction. In addition, the ground fissure area extracted by ENVINet5‐OBIC closely aligns with ground truth data. This study provides a more effective method for automatic extraction of ground fissures, which improves the efficiency of environmental monitoring in mining areas.

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MFPA-Net: An efficient deep learning network for automatic ground fissures extraction in UAV images of the coal mining area

Cited by 10 publications

References 26 publications

Identification of Ground Fissure Development in a Semi-Desert Aeolian Sand Area Induced from Coal Mining: Utilizing UAV Images and Deep Learning Techniques

Identification of Ground Fissure Development in a Semi-Desert Aeolian Sand Area Induced from Coal Mining: Utilizing UAV Images and Deep Learning Techniques

An Image Feature Extraction Algorithm Based on Tissue P System

Automated extraction of mining‐induced ground fissures using deep learning and object‐based image classification

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