Search Results (18)

The Battle of Rhode Island in 1778 was an important event in the revolutionary war leading to the international recognition of U.S. American independence following the 1776 declaration. It culminated in a month-long campaign against British forces occupying Aquidneck Island, serving as the first combined operation of the newly formed Franco-American alliance. The military fortification at Butts Hill in Portsmouth, Rhode Island, served as a strategic point during the conflict and remains well-conserved today. While LiDAR has assisted in the geospatial surface reconstruction of the site’s earthwork fortifications, it is unknown whether other historically documented buildings within the fort remain preserved underground. We therefore conducted a ground-penetrating radar (GPR) survey to ascertain the presence or absence of architectural features, hypothesizing that GPR imaging could reveal structural remnants from the military barracks constructed in 1777. To test this hypothesis, we used public satellite and LiDAR imagery alongside historical maps to target the location of the historical barracks, creating a grid to survey the area with a GPR module in 0.5 m transects. Our results, superimposing remote sensing imagery with historical maps, indicate that the remains of a barracks building are likely present between circa 5–50 cm beneath today’s surface, warranting future investigations. Full article

Mine fire is caused by external heat source or coal seam spontaneous combustion, and there are serious hidden dangers in mining operation. The existing detection methods have high cost, limited coverage and delayed response. An edge intelligent fire detection system based on multi-source information fusion is proposed. We enhance the YOLOv5s backbone network by (1) optimized small-target detection and (2) adaptive attention mechanism to improve recognition accuracy. In order to overcome the limitation of video only, a dynamic weighting algorithm combining video and multi-sensor data is proposed, which adjusts the strategy according to the real-time fire risk index. Deploying quantitative models on edge devices can improve underground intelligence and response speed. The experimental results show that the improved YOLOv5s is 7.2% higher than the baseline, the detection accuracy of the edge system in the simulated environment is 8.28% higher, and the detection speed is 26% higher than that of cloud computing. Full article

Ground-Penetrating Radar (GPR) is a non-destructive detection technique widely employed for identifying underground targets. Despite its utility, conventional approaches suffer from limitations, including poor adaptability to multi-scale targets and suboptimal localization accuracy. To overcome these challenges, we propose a lightweight deep learning framework, the Dual Attentive YOLOv11 (You Only Look Once, version 11) Keypoint Detector (DAYKD), designed for robust underground target detection and precise localization. Building upon the YOLOv11 architecture, our method introduces two key innovations to enhance performance: (1) a dual-task learning framework that synergizes bounding box detection with keypoint regression to refine localization precision, and (2) a novel Convolution and Attention Fusion Module (CAFM) coupled with a Feature Refinement Network (FRFN) to enhance multi-scale feature representation. Extensive ablation studies demonstrate that DAYKD achieves a precision of 93.7% and an mAP50 of 94.7% in object detection tasks, surpassing the baseline model by about 13% in F1-score, a balanced metric that combines precision and recall to evaluate overall model performance, underscoring its superior performance. These findings confirm that DAYKD delivers exceptional recognition accuracy and robustness, offering a promising solution for high-precision underground target localization. Full article

Smoke appears earlier than flames, so image-based fire monitoring techniques mainly focus on the detection of smoke, which is regarded as one of the effective strategies for preventing the spread of initial fires that eventually evolve into serious fires. Smoke monitoring in mine fires faces serious challenges: the underground environment is complex, with smoke and backgrounds being highly integrated and visual features being blurred, which makes it difficult for existing image-based monitoring techniques to meet the actual needs in terms of accuracy and robustness. The conventional ground-based methods are directly used in the underground with a high rate of missed detection and false detection. Aiming at the core problems of mixed target and background information and high boundary uncertainty in smoke images, this paper, inspired by the principle of information entropy, proposes a method for recognizing smoke from mine fires by integrating entropy-enhanced image processing and improved YOLOv8. Firstly, according to the entropy change characteristics of spatio-temporal information brought by smoke diffusion movement, based on spatio-temporal entropy separation, an equidistant frame image differential fusion method is proposed, which effectively suppresses the low entropy background noise, enhances the detail clarity of the high entropy smoke region, and significantly improves the image signal-to-noise ratio. Further, in order to cope with the variable scale and complex texture (high information entropy) of the smoke target, an improvement mechanism based on entropy-constrained feature focusing is introduced on the basis of the YOLOv8m model, so as to more effectively capture and distinguish the rich detailed features and uncertain information of the smoke region, realizing the balanced and accurate detection of large and small smoke targets. The experiments show that the comprehensive performance of the proposed method is significantly better than the baseline model and similar algorithms, and it can meet the demand of real-time detection. Compared with YOLOv9m, YOLOv10n, and YOLOv11n, although there is a decrease in inference speed, the accuracy, recall, average detection accuracy mAP (50), and mAP (50–95) performance metrics are all substantially improved. The precision and robustness of smoke recognition in complex mine scenarios are effectively improved. Full article

Ground penetrating radar (GPR) is an effective and efficient nondestructive testing technology for subsurface investigations. Deep learning-based methods have been successfully used in automatic underground target recognition. However, these methods are mostly based on supervised learning, requiring large amounts of labeled training data to guarantee high accuracy and generalization ability, which is a challenge in GPR fields due to time-consuming and labor-intensive data annotation work. To alleviate the demand for abundant labeled data, a semi-supervised deep learning method named attention–temporal ensembling (Attention-TE) is proposed for underground target recognition using GPR B-scan images. This method integrates a semi-supervised temporal ensembling architecture with a triplet attention module to enhance the classification performance. Experimental results of laboratory and field data demonstrate that the proposed method can automatically recognize underground targets with an average accuracy of above 90% using less than 30% of labeled data in the training dataset. Ablation experimental results verify the efficiency of the triplet attention module. Moreover, comparative experimental results validate that the proposed Attention-TE algorithm outperforms the supervised method based on transfer learning and four semi-supervised state-of-the-art methods. Full article

Accurate detection of underground grounding lines remains a significant technical challenge due to their deep burial and small cross-sectional dimensions, which cause signal scattering in heterogeneous soil media. This results in blurred features in GPR B-scan images, impeding reliable target identification. To address this limitation, we propose GPR-TSBiNet, an architecture incorporating two key model innovations. We introduce GPR-Transformer (GPR-Trans), a multi-branch backbone network specifically designed for GPR B-scan processing. In the neck stage, we develop the Spatial-Depth Converted Bidirectional Feature Pyramid Network (SC-BiFPN), which integrates SPD-ADown to mitigate feature loss caused by traditional pooling-based downsampling. We employ Shape-IoU as the loss function to enhance boundary detail preservation for small targets. Comparative experiments demonstrate that GPR-TSBiNet outperforms state-of-the-art (SOTA) models YOLOv11 and YOLOv10 in detection accuracy, achieving an AP0.5 improvement of 11.6% over YOLOv11X and 27.4% over YOLOv10X. Notably, the model improves small-target APsmall to 49.4 ± 0.7%, representing a 13.4% increase over the SOTA YOLOv11 model. Finally, real-world GPR validation experiments are conducted, confirming that GPR-TSBiNet provides a reliable solution for underground grounding line detection in GPR-based target recognition. Full article

With the widespread recognition of underground salt cavern compressed air storage at home and abroad, how to choose and evaluate salt cavern resources has become a key issue in the construction of gas storage. This paper discussed the condition of building power plants, the collection of regional data and salt plant data, and the analysis of stability and tightness. Comprehensive analysis and evaluation methods were put forward from four aspects, including ground comprehensive conditions, regional geological conditions and formation lithology, salt mine characteristics, stability, and tightness of salt caverns. The limit equilibrium theory was applied to establish the limit equilibrium failure mode of salt caverns under operating pressure, and the stability coefficient calculation method of the target salt cavern was determined by combining the mechanical characteristics. Based on the physical and mechanical properties of salt rocks, it was found that salt rocks with enough thickness around the salt cavity could be used as sealing rings to ensure the tightness of the salt cavern. Combined with the field water sealing test, the tightness of the target salt cavern is verified. This method has been applied to the salt cavern screening and evaluation of a 300 MW compressed air energy storage power plant project in Yingcheng, Hubei Province, and remarkable results have been obtained, indicating the rationality of the method. Full article

This study proposes a new approach to gait recognition using LoRa signals, taking into account the challenging conditions found in underground coal mines, such as low illumination, high temperature and humidity, high dust concentrations, and limited space. The aim is to address the limitations of existing gait recognition research, which relies on sensors or other wireless signals that are sensitive to environmental factors, costly to deploy, invasive, and require close sensing distances. The proposed method analyzes the received signal waveform and utilizes the amplitude data for gait recognition. To ensure data reliability, outlier removal and signal smoothing are performed using Hampel and S-G filters, respectively. Additionally, high-frequency noise is eliminated through the application of Butterworth filters. To enhance the discriminative power of gait features, the pre-processed data are reconstructed using an autoencoder, which effectively extracts the underlying gait behavior. The trained autoencoder generates encoder features that serve as the input matrix. The Softmax method is then employed to associate these features with individual identities, enabling LoRa-based single-target gait recognition. Experimental results demonstrate significant performance improvements. In indoor environments, the recognition accuracy for groups of 2 to 8 individuals ranges from 99.7% to 96.6%. Notably, in an underground coal mine where the target is located 20 m away from the transceiver, the recognition accuracy for eight individuals reaches 93.3%. Full article

The detection of coal and gangue is an essential part of intelligent sorting. A lightweight coal and gangue detection algorithm based on You Only Look Once version 5s (Yolov5s) is proposed for the current coal and gangue target detection algorithm with the low accuracy of small target detection, high model complexity, and sizeable computational memory consumption. Firstly, we build a new convolutional block based on the Funnel Rectified Linear Unit (FReLU) activation function and apply it to the original Yolov5s network so that the model adaptively captures local contextual information of the image. Secondly, the neck of the original network is redesigned to improve the detection accuracy of small samples by adding a small target detection head to achieve multi-scale feature fusion. Next, some of the standard convolution modules in the original network are replaced with Depthwise Convolution (DWC) and Ghost Shuffle Convolution (GSC) modules to build a lightweight feature extraction network while ensuring the model detection accuracy. Finally, an efficient channel attention (ECA) module is embedded in the backbone of the lightweight network to facilitate accurate localization of the prediction region by improving the information interaction of the model with the channel features. In addition, the importance of each component is fully demonstrated by ablation experiments and visualization analysis comparison experiments. The experimental results show that the mean average precision (mAP) and the model size of our proposed model reach 0.985 and 4.9 M, respectively. The mAP is improved by 0.6%, and the number of parameters is reduced by 72.76% compared with the original Yolov5s network. The improved algorithm has higher localization and recognition accuracy while significantly reducing the number of floating-point calculations and of parameters, reducing the dependence on hardware, and providing a specific reference basis for deploying automated underground gangue sorting. Full article

Ground-penetrating radar (GPR) is an important nondestructive testing (NDT) tool for the underground exploration of urban roads. However, due to the large amount of GPR data, traditional manual interpretation is time-consuming and laborious. To address this problem, an efficient underground target detection method for urban roads based on neural networks is proposed in this paper. First, robust principal component analysis (RPCA) is used to suppress the clutter in the B-scan image. Then, three time-domain statistics of each A-scan signal are calculated as its features, and one backpropagation (BP) neural network is adopted to recognize A-scan signals to obtain the horizontal regions of targets. Next, the fusion and deletion (FAD) algorithm is used to further optimize the horizontal regions of targets. Finally, three time-domain statistics of each segmented A-scan signal in the horizontal regions of targets are extracted as the features, and another BP neural network is employed to recognize the segmented A-scan signals to obtain the vertical regions of targets. The proposed method is verified with both simulation and real GPR data. The experimental results show that the proposed method can effectively locate the horizontal ranges and vertical depths of underground targets for urban roads and has higher recognition accuracy and less processing time than the traditional segmentation recognition methods. Full article

The drilling robot is the key equipment for pressure relief in rockburst mines, and the accurate recognition of a pressure relief hole is the premise for optimizing the layout of pressure relief holes and intelligent drilling. In view of this, a pressure relief hole recognition method for a drilling robot, based on single-image generative adversarial network (SinGAN) and improved faster region convolution neural network (Faster R-CNN), is proposed. Aiming at the problem of insufficient sample generation diversity and poor performance of the traditional SinGAN model, some improvement measures including image size adjustment, multi-stage training, and dynamically changing iteration times are designed as an improved SinGAN for the generation of pressure relief hole images. In addition, to solve the problem that the traditional depth neural network is not ideal for small-size target recognition, an improved Faster R-CNN based on multi-scale image input and multi-layer feature fusion is designed with the improved SqueezeNet as the framework, and the sample data collected from ground experiments are used for comparative analysis. The results indicate that the improved SinGAN model can improve the diversity of generated images on the premise of ensuring the quality of image samples, and can greatly improve the training speed of the model. The accuracy and recall rate of the improved Faster R-CNN model were able to reach 90.09% and 98.32%, respectively, and the average detection time was 0.19 s, which verifies the superiority of the improved Faster R-CNN model. To further verify the practicability of the proposed method, some field images were collected from the underground rockburst relief area in the coal mine, and a corresponding test analysis was carried out. Compared with three YOLO models, the accuracy and recall rate of improved Faster R-CNN model improved significantly, although the training time and recognition time increased to a certain extent, which proves the feasibility and effectiveness of the proposed method. Full article

The belt conveyor is the main equipment for underground coal transportation. Its coal flow is mixed with large coal, gangue, anchor rods, wooden strips, and other foreign objects, which easily causes failure of the conveyor belt, such as scratching, tearing, and even broken belts. Aiming at the problem that it was difficult to accurately identify the foreign objects of underground belt conveyors due to the influence of fog, high-speed operation, and obscuration, the coal mine belt conveyor foreign object recognition method of improved YOLOv5 algorithm with defogging and deblurring was proposed. In order to improve the clarity of the monitoring video of the belt conveyor, the dark channel priori defogging algorithm is applied to reduce the impact of fog on the clarity of the monitoring video, and the image is sharpened by user-defined convolution method to reduce the blurring effect on the image in high-speed operation condition. In order to improve the precision of foreign object identification, the convolution block attention module is used to improve the feature expression ability of the foreign object in the complex background. Through adaptive spatial feature fusion, the multi-layer feature information of the foreign object image is more fully fused so as to achieve the goal of accurate recognition of foreign objects. In order to verify the recognition effect of the improved YOLOv5 algorithm, a comparative test is conducted with self-built data set and a public data set. The results show that the performance of the improved YOLOv5 algorithm is better than SSD, YOLOv3, and YOLOv5. The belt conveyor monitoring video of resolution for 1920 × 1080 in Huangling Coal Mine is used for identification verification, the recognition accuracy can reach 95.09%, and the recognition frame rate is 56.50 FPS. The improved YOLOv5 algorithm can provide a reference for the accurate recognition of targets in a complex underground environment. Full article

After wars, some unexploded bombs remained underground, and these faulty bombs seriously threaten the safety of people. The ability to accurately identify targets is crucial for subsequent mining work. A deep learning algorithm is used to recognize targets, which significantly improves recognition accuracy compared with the traditional recognition algorithm for measuring the magnetic moment of the target and the included geomagnetism angle. In this paper, a ResNet-18-based recognition system is presented for classifying metallic object types. First, a fluxgate magnetometer cube arrangement structure (FMCAS) magnetic field feature collector is constructed, utilizing an eight-fluxgate magnetometer sensor array structure that provides a 400 mm separation between each sensitive unit. Magnetic field data are acquired, along an east–west survey line on the northern side of the measured target using the FMCAS. Next, the location and type of targets are modified to create a database of magnetic target models, increasing the diversity of the training dataset. The experimental dataset is constructed by constructing the magnetic flux density tensor matrix. Finally, the enhanced ResNet-18 is used to train the data for the classification recognition recognizer. According to the test findings of 107 validation set groups, this method’s recognition accuracy is 84.1 percent. With a recognition accuracy rate of 96.3 percent, a recall rate of 96.4 percent, and a precision rate of 96.4 percent, the target with the largest magnetic moment has the best recognition impact. Experimental findings demonstrate that our enhanced RestNet-18 network can efficiently classify metallic items. This provides a new idea for underground metal target identification and classification. Full article

Ground penetrating radar (GPR) detection is a popular technology in civil engineering. Because of its advantages of non-destructive testing (NDT) and high work efficiency, GPR is widely used to detect hard foreign objects in soil. However, the interpretation of GPR images relies heavily on the work experience of researchers, which may lead to problems of low detection efficiency and a high false recognition rate. Therefore, this paper proposes a real-time detection technology of GPR based on deep learning for the application of soil foreign object detection. In this study, the GPR image signal is obtained in real time by the GPR instrument and software, and the image signals are preprocessed to improve the signal-to-noise ratio of the GPR image signals and improve the image quality. Then, in view of the problem that YOLOv5 poorly detects small targets, this study improves the problems of false detection and missed detection in real-time GPR detection by improving the network structure of YOLOv5, adding an attention mechanism, data enhancement, and other means. Finally, by establishing a regression equation for the position information of the ground penetrating radar, the precise localization of the foreign matter in the underground soil is realized. Full article

Grounding facilities, including high-voltage DC grounding electrodes and auxiliary anodes in impressed current cathodic protection systems, inject current into the ground. This study developed an experimental platform to determine the safe limit of current density for such facilities through an analysis of fish behavior on the platform. Zebrafish (Brachydanio rerio) were selected for the experiment and placed in a tank; two rod electrodes were used to inject direct current into the water. A wireless camera was focused on the water tank to video record possible changes in fish behavior. The output voltage of the DC power source was varied, and the trajectories of the fish under various direct current fields were recorded. A tracking program was developed to analyze the trajectories and quantify the behavior of the fish. A new method combining the trajectories of fish samples with the results of current density calculations for analysis was proposed. Results demonstrated that the zebrafish could sense current in the water and turn when exposed to certain current densities. The intensity of the current at the turning points was statistically analyzed, and the threshold of current density at which the fish could no longer tolerate the current and turned was 0.4231 A/m². Full article