Hole Detection Research Articles

Aquaculture defects recognition via multi-scale semantic segmentation

Aquaculture net pen defects such as biofouling, vegetation, and holes are key challenges to efficient and sustainable fish production in aquaculture. These defects must be monitored to avoid problems with aquaculture net safety as well as fish growth. Recently, deep learning methods have been adopted to solve detection and classification problems in different applications. However, the conventional methods are challenging to meet the demands of high precision and real-time detection of aquaculture net defects in a complex marine environment. Towards this end, this paper proposes an autonomous net pen defect detection system that contains a novel multi-scale semantic segmentation topology for detecting the biofouling, vegetation, and hole problems in the aquaculture environment. In particular, we emphasize fusing the attention maps obtained across different decomposition levels of the network to generate rich feature distributions that enable the accurate extraction of biofouling, vegetation, and holes within aquaculture nets. Moreover, the proposed model is thoroughly tested on two private datasets and two public datasets which were acquired from a real-field fish farms. Across all four datasets, the proposed framework showed remarkable biofouling, vegetation, and hole detection performance, where it outperformed state-of-the-art methods by 6.58%, 3.69%, 6.44%, and 4.78% in terms of mean average precision across LABUST, KU, NDv1, and NDv2 datasets, respectively.

A binocular vision method for precise hole recognition in satellite assembly systems

The prevalent manual assembly process for satellites is marked by inefficiencies and a heightened risk of collision, thus limiting its scalability and potential for integration. To address these limitations, the adoption of industrial robots for an intelligent assembly mode has emerged as a promising alternative. Nonetheless, the implementation of an industrial robot satellite assembly system brings its own challenges, including low accuracy of the robot, large manufacturing errors of the satellite products and accumulated errors in the picking and placing process. As the main positioning feature, the detection of holes plays an important role in solving these problems. This paper introduces a novel binocular vision hole recognition system, coupled with an associated hole recognition algorithm, aiming at enhancing the detection accuracy of characteristic holes of satellite products. The proposed methodology includes the development of a confidence-based indicator to assess the quality of two-dimensional hole detection, and an iterative optimization of the fitting ellipse. Furthermore, a confidence-based neighborhood denoising two-stage fitting method is designed for accurate hole positioning. Experimental results validate the proposed methodology, showcasing a recognition accuracy of 0.060 mm and an assembly accuracy within 0.2 mm, thereby fulfilling the requirements of the industrial robot satellite assembly system.

Hours-long Near-UV/Optical Emission from Mildly Relativistic Outflows in Black Hole–Neutron Star Mergers

The ongoing LIGO–Virgo–KAGRA observing run O4 provides an opportunity to discover new multimessenger events, including binary neutron star (BNS) mergers such as GW170817 and the highly anticipated first detection of a multimessenger black hole–neutron star (BH–NS) merger. While BNS mergers were predicted to exhibit early optical emission from mildly relativistic outflows, it has remained uncertain whether the BH–NS merger ejecta provides the conditions for similar signals to emerge. We present the first modeling of early near-ultraviolet/optical emission from mildly relativistic outflows in BH–NS mergers. Adopting optimal binary properties, a mass ratio of q = 2, and a rapidly rotating BH, we utilize numerical relativity and general relativistic magnetohydrodynamic (GRMHD) simulations to follow the binary’s evolution from premerger to homologous expansion. We use an M1 neutrino transport GRMHD simulation to self-consistently estimate the opacity distribution in the outflows and find a bright near-ultraviolet/optical signal that emerges due to jet-powered cocoon cooling emission, outshining the kilonova emission at early time. The signal peaks at an absolute magnitude of ∼−15 a few hours after the merger, longer than previous estimates, which did not consider the first principles–based jet launching. By late 2024, the Rubin Observatory will have the capability to track the entire signal evolution or detect its peak up to distances of ≳1 Gpc. In 2026, ULTRASAT will conduct all-sky surveys within minutes, detecting some of these events within ∼200 Mpc. The BH–NS mergers with higher mass ratios or lower BH spins would produce shorter and fainter signals.

Spotlight on Lattice Degeneration Imaging Techniques.

Lattice degeneration (LD), routinely diagnosed with indirect ophthalmoscopy, is one of the most common and clinically significant peripheral retinal findings. In this review, we have summarized the data on currently available imaging techniques which help to improve diagnosis and our understanding of LD pathogenesis. Ultra-wide field imaging provides reliable color fundus capturing for the primary diagnosis of LD and may also be used as a screening tool. Wide-field imaging can be used for targeted documentation of LD lesions using true colors and with minimal optical distortions. Information on the status of the vitreoretinal interface, including detection of retinal holes, detachments, and vitreous tractions, can be obtained with peripheral structural optical coherence tomography (OCT) or scanning laser ophthalmoscopy in retro-mode. These techniques clarify the associated risks of rhegmatogenous retinal detachment. Fundus autofluorescence can provide details on atrophic changes. However, the risk of retinal detachment by means of this technique requires further investigation. OCT angiography may be successfully performed for some lesions. Taken together, OCT and OCT angiography demonstrate thinning of the choroid, alteration of local choroidal microcirculation, and, in severe lesions, involvement of the sclera. OCT angiography confirms loss of retinal microcirculation within LD lesion, which was previously shown with fluorescein angiography. In conclusion, despite relatively simple primary diagnosis, imaging of LD lesions remains challenging due to their peripheral localization. However, several new strategies, including ultra-wide field imaging, peripheral OCT, and scanning laser ophthalmoscopy, make LD imaging possible on a routine basis, improving diagnosis and understanding of LD pathogenesis.

Detection of defects in polyethylene pipes using open microwave coaxial line resonator sensor

A new method using a microwave coaxial line resonator sensor for the non-destructive evaluation of defects in polyethylene pipes is presented. The sensor consists of a coaxial line section and an extended section of circular waveguide. One end is short-circuited and the other end is open to free space. The sensor aperture is designed to conform to the curvature of the pipe outer surface, so that the common problem of the standoff distance effect by open-ended waveguide scanning is well circumvented. The presence of defects causes local permittivity and volume changes, leading to variations of the resonance frequency and quality factor due to material perturbation. The detection principle is confirmed by electromagnetic simulation. From the experiments, it is shown that the proposed sensor can perform both detection and classification of flat-bottom holes. The minimum diameter of the hole that can be detected is 1 mm, which is lower than that of a hole required in the standard practice. This method can also effectively detect axial defects and determine the lengths. In addition, an open-ended waveguide is used to detect axial holes, and the results were compared with those of coaxial line resonator. The sensor system presented here has the advantages of low cost, convenience for on-site detection and quantitative analysis.

Hole Detection in Plastic Mulch Using Template Matching and Machine Learning Algorithms

Mulch is a ground cover material to maintain soil moisture and temperature stability as a plant medium. Mulch also helps prevent weed growth for better plant growth. For planting with plastic mulch, farmers need to make holes in the mulch the day before planting. Precision agriculture is needed because it can obtain savings in input financing, labor, and better yields, so this research aims to identify holes in mulch based on Unmanned Aerial Vehicle images. The advantage of this research is that it can monitor each plant based on the mulch holes, and the number of holes identified can be used as a parameter to estimate the amount of crop production. This research combines Template Matching Algorithm and Machine Learning Algorithm to improve accuracy in predicting holes in mulch. Three machine learning algorithms are used, namely the Random Forest, Support Vector Machine, and XGBoost. The data used is an orthophoto mosaic from aerial photographs. Nine areas were taken from orthophotos to be used as research samples. The results of this study obtained the highest average recall, precision, and f-measure values using the Support Vector Machine algorithm with a recall value of 87.7%, precision of 97.5%, and f-score of 92.3%. This research focuses on reducing detected commission errors. Therefore, omission errors were still detected in the damaged or leaf-covered holes.

Geomembrane leaks detection and leakage correlation factor analysis of composite liner systems for fifty-five (55) solid waste landfills in China

In composite liner system (CLS), there are little field data on the number, size, and shape of geomembrane (GMB) holes, which are the key parameters to identify and evaluate the volume of leachate leakage from landfills. In this paper, in-situ detection of GMB holes in composite liner systems of 55 solid waste landfills in China was conducted by electrical leak location (ELL) method, and the following conclusions were drawn. The holes frequency, equivalent radius, and theoretical leakage volume of double-layer systems are lower than those of single-layer systems under the same geotextile (GT) density by 28.2%, 51.4%, and more than 60%, respectively. As the thickness of GMB increases, the holes frequency, equivalent radius and theoretical leakage volume reduce observably. Regarding protecting GMB, the density of geotextiles should not be less than 600 g/m2. Through the analysis of various correlation factors, the thickness of geomembrane is an important parameter affecting the amount of leakage. This study will provide a reference for analyzing the data of field investigation, which would provide constructive suggestions for the composite liner system.

Vasicek and Van Es entropy‐based spectrum sensing for cognitive radios

AbstractAccurate detection of spectrum holes is a useful requirement for cognitive radios that improves the efficiency of spectrum usage. The authors propose three novel, simple, and entropy‐based detectors for spectrum sensing in cognitive radio. The authors evaluate the probability of detection of these three detectors: Vasicek's entropy detector, truncated Vasicek's entropy detector, and Van Es' entropy detector, over a predefined probability of false‐alarm. In particular, the authors provide the approximate and asymptotic test statistics for these detectors in the presence and absence of Nakagami‐m fading, noise variance uncertainty, and optimised detection threshold. Furthermore, the authors provide a detailed comparison study among all the detectors via Monte Carlo simulations and justify authors results through real‐world data. The authors’ experimental results establish a superior performance of truncated Vasicek's entropy detector over Vasicek's entropy detector, energy detector, differential entropy detector and Van Es' entropy detector in practically viable scenarios.

LPO-YOLOv5s: A Lightweight Pouring Robot Object Detection Algorithm.

The casting process involves pouring molten metal into a mold cavity. Currently, traditional object detection algorithms exhibit a low accuracy and are rarely used. An object detection model based on deep learning requires a large amount of memory and poses challenges in the deployment and resource allocation for resource limited pouring robots. To address the accurate identification and localization of pouring holes with limited resources, this paper designs a lightweight pouring robot hole detection algorithm named LPO-YOLOv5s, based on YOLOv5s. First, the MobileNetv3 network is introduced as a feature extraction network, to reduce model complexity and the number of parameters. Second, a depthwise separable information fusion module (DSIFM) is designed, and a lightweight operator called CARAFE is employed for feature upsampling, to enhance the feature extraction capability of the network. Finally, a dynamic head (DyHead) is adopted during the network prediction stage, to improve the detection performance. Extensive experiments were conducted on a pouring hole dataset, to evaluate the proposed method. Compared to YOLOv5s, our LPO-YOLOv5s algorithm reduces the parameter size by 45% and decreases computational costs by 55%, while sacrificing only 0.1% of mean average precision (mAP). The model size is only 7.74 MB, fulfilling the deployment requirements for pouring robots.

Gravitational ringing and superradiant instabilities of the Kerr-like black holes in a dark matter halo

Supermassive black holes from the center of galaxy may be immersed in a dark matter halo. This dark matter halo may form a “cusp” structure around the black hole and disappear at a certain distance from the black hole. Based on this interesting physical background, we use the continued fraction method to study gravitational ringring of the Kerr-like black holes immersed in a dark matter halo, i.e., quasinormal modes (QNM) and quasibound states (QBS). We consider these gravitational ringring of black holes both in cold dark matter (CDM) model and scalar field dark matter (SFDM) model at the LSB galaxy, and compare them with Kerr black hole. By testing the states of QNM/QBS frequencies with different parameters l, m, a, we confirm the existence of the superradiant instabilities when the black holes both in CDM model and SFDM model. Besides, we also study the impacts of dark matter parameters on the QNM/QBS of black holes at the specific circumstances. In the future, these results may be used for gravitational wave detection of supermassive black holes, and may provide an effective method for detecting the existence of dark matter.

Application of electrical resistivity tomography (ERT) for vertical high-density polyethylene (HDPE) membrane detection

At landfill sites, uncontrolled leachate is a serious cause of groundwater and soil contamination at landfills and can occur extensively when there are looploopholesin the vertical HDPE (High-density polyethylene) membrane. For this reason, detecting HDPE membranes is necessary. The conventional electrode running poles approach is limited, it reflects the distribution of resistivity in 2D subsurface profiles, which is difficult to deploy electrodes above vertical HDPE membrane. According to the structural characteristics and field conditions of vertical HDPE membranes, the two-row current and potential (CP) electrode array is proposed to be applied to the testing of vertical HDPE membranes. The forward geoelectric model of the vulnerability was built using pyGimli, and data on 1519 apparent resistivity were acquired. The previous knowledge without the vulnerability was taken into consideration as the constraint condition, and the regional control was added to conduct the inversion, all using the least square approach. The model was used to analyze data on the detection of vertical HDPE membranes in a landfill In Yancheng, and potential flaws were discovered. Results show that the CP electrical measuring device can be efficient, quick, and accurate in the positioning of vertical HDPE membrane hole detection, and it is a cost-effective method. To verify the results, in the vertical HDPE membrane, through the Wenner device, the inversion of the 2D profile, and low resistivity anomalies are detected. This is thought to be through the leachate of leakage of HDPE membrane resistance rate is about 1.8 ohm. The findings demonstrate that the vertical HDPE membrane susceptibility may be located and detected using a CP electrical measuring instrument, which is a practical and efficient approach.

Ultrasonic defect detection of high-density polyethylene pipe materials using FIR filtering and block-wise singular value decomposition

Condition monitoring of high-density polyethylene (HDPE) pipes used for fluid and gas transfer is important for the safety of energy conservation and the environment. Ultrasonic phased array imaging methods provide a solution to detect and assess defects in HDPE pipes. However, ultrasonic bulk waves propagating in these viscoelastic media are strongly attenuated, resulting in reduced signal amplitude. In this study, a linear-phase Finite Impulse Response (FIR) filter is used to remove unwanted frequency components from the measured ultrasonic signals to improve the signal-to-noise ratio before applying the imaging algorithm of the total focusing method (TFM). Building upon this, a block-wise singular value decomposition (SVD) technique, which can adaptively determine the singular value cutoff threshold based on each block in the whole TFM image, is used to enhance the obtained TFM image quality. The performance of the combination of FIR filtering and block-wise SVD technique is validated by experimental data of HDPE pipe materials. Results demonstrate that the proposed approach generates good images to provide the detection and characterization of side-drilled holes in HDPE pipe materials.

Research on Sustainable Mining and Water Prevention in Large Open-Pit Water Deposits

Due to the impacts on the ecological environment, production safety and the economic benefits of large open-pit water deposits, there is no longer a single drainage method within the pit that can be used to meet the need for further deep mining. Therefore, curtain grouting technology is proposed for use in the prevention and control of mine water. The flow control equation of slurry in vertical fissures under the influence of self-weight stress was derived, and a three-dimensional visualized laminated jointed rock grouting model was developed independently to study the slurry transport mechanism, diffusion pattern, and sealing mechanism, which verified the correctness of the control equation. Field trials of industrial curtain grouting were also carried out at the mines, and the effectiveness of curtain water blockage was tested using visual analysis, inspection hole detection, and geophysical exploration methods. The results showed that after grouting the curtain, water-conducting cracks in the formation can be filled well and sealed, and the curtain has a good water-blocking effect. A complete set of diversified value-taking grouting systems is obtained, which can ensure the development of a green, safe and sustainable mine in the future.

Node position estimation based on optimal clustering and detection of coverage hole in wireless sensor networks using hybrid deep reinforcement learning

Node position estimation based on optimal clustering and detection of coverage hole in wireless sensor networks using hybrid deep reinforcement learning

P‐135: The AI based auto masking technique for detection of small stage vacuum hole †

In order to improve productivity in the display lamination process, it was necessary to find the vacuum holes on the stage and perform masking treatment. Although the vacuum holes were detected by employing a conventional image processing technique, there was a problem that it was impossible to detect all of them. Therefore, we required the AI object detection technology that can solve the problem. However, the hole has a size of 11 × 11 px and corresponds to the tiny size which is the most difficult for small object detection. For the development of AI auto masking technology, YOLOv5 was selected as the AI object detector because it was light, fast and had good performance, and SAHI technology, which had excellent performance in detecting small objects and excellent compatibility with the various detectors, was selected. The hyper‐parameters tuning and various optimization approaches were performed on AI techniques and the AI auto masking technology was developed. As a result, the time to detect vacuum hole was reduced to 20% compared to the conventional image processing technique. In the SAHI technique, the direct proportion between the slice width/height size and bounding box size of the detected object was found, and the application of the image based accurate object size was also discussed.

Comparative analysis of blind detectors in a cluster-based cooperative Spectrum Hole detection

Prevention of authorized users from interference determine the accurate detection of Spectrum Hole (SH) is of great importance in a Spectrum Shearing Network (SSN). However, multipath fading and shadowing affect the accurate detection of SH resulting in interference. Cluster-Based Cooperative Spectrum Hole Detection (CBCSHD) used to address this problem depends on detector and number of clusters. Hence, comparative analysis of blind detectors in CBCSHD is carried out to evaluate its performance with various blind detectors and number of clusters. The CBCSHD is carried out using six Cognitive Users (CUs) that jointly carry out detection of SH and each of the CUs performs local sensing using Eigenvalue Detector (EVD), Energy Detector (ED) and Cyclostationary Detector (CD). The CUs form clusters to reduce reporting overhead between CUs. The local sensing results from individual user are combined at the Cluster Head (CH) using majority fusion rule. The performance of each of the detectors in CBCSHD is evaluated using Probability of Detection (PD) and Sensing Time (ST). PD values of 0.7661, 0.7160 and 0.6229 are obtained at SNR of 4 dB for ED, CD and EVD, respectively, while ST values of 3.0707, 3.7163 and 4.0907 s are obtained for ED, CD and EVD, respectively. The results obtained show that ED has the highest detection rate, followed by CD, while EVD shows the worst detection rate.

The Different Welding Layers and Heat Source Energy on Residual Stresses in Swing Arc Narrow Gap MAG Welding.

In this paper, in order to reduce the time cost of prediction experiments in industry, a new narrow gap oscillation calculation method is developed in ABAQUS thermomechanical coupling analysis to study the distribution trend of residual weld stresses in comparison with conventional multi-layer welding processes. The blind hole detection technique and thermocouple measurement method verify the reliability of the prediction experiment. The results show that the experimental and simulation results have a high degree of agreement. In the prediction experiments, the calculation time of the high-energy single-layer welding experiments is 1/4 of the traditional multi-layer welding. Two welding processes of longitudinal residual stress and transverse residual stress distribution trends are the same. The high-energy single-layer welding experiment stress distribution range and transverse residual stress peak are smaller, but the longitudinal residual stress peak is slightly higher, which can be effectively reduced by increasing the preheating temperature of the welded parts. This implies that in the specific case of increasing the initial temperature of the workpiece, the use of high-energy single-layer welding instead of multi-layer welding to study the residual stress distribution trend not only optimizes the weld quality but also reduces the time cost to a large extent.

DHD-MEPO: A Novel Distributed Coverage Hole Detection and Repair Method for Three-Dimensional Hybrid Wireless Sensor Networks

A coverage hole is a problem that cannot be completely avoided in three-dimensional hybrid wireless sensor networks. It can lead to hindrances in monitoring tasks and adversely affect network performance. To address the problem of coverage holes caused by the uneven initial deployment of the network and node damage during operation, we propose a distributed hole detection and multi-objective optimization emperor penguin repair algorithm (DHD-MEPO). In the detection phase, the monitoring region is zoned as units according to the quantity of nodes and the sensing range, and static nodes use the sum-of-weights method to campaign for group nodes on their terms, determining the location of holes by calculating the coverage of each cell. In the repair phase, the set of repair nodes is determined by calculating the mobile node coverage redundancy. Based on the characteristics of complex environments, the regions of high hole levels are prioritized. Moreover, the residual energy homogeneity of nodes is considered for the design of multi-objective functions. A lens-imaging mapping learning strategy is introduced to perturb the location of repair nodes for the optimization of the emperor penguin algorithm. Experimental results illustrate that the DHD-MEPO, compared with the C-CICHH, 3D-VPCA, RA, EMSCOLER, and IERP algorithms, can balance the uniformity of the residual energy of each node while satisfying the network coverage requirements and network connectivity, which effectively improves the network coverage performance.

Modified Cuckoo Search and Hill Climbing Algorithm Based Spectrum Hole Detection in Cognitive Radio Networks

Modified Cuckoo Search and Hill Climbing Algorithm Based Spectrum Hole Detection in Cognitive Radio Networks

Toward the automatic detection of effective gerbil holes in desert grasslands through unmanned aerial vehicle imagery

Rapidly determining the density of effective gerbil holes is ecologically important but technically challenging. However, unmanned aerial vehicles (UAVs) offer new methods to identify effective gerbil holes and understand the spatial relationships between gerbils and grass coverage. In this study, we focused on Meriones unguiculatus, which live in desert grasslands of Ordos, and gathered UAV imagery to explore the performance of rule-based classification in object-based image analysis (OBIA) for identifying effective gerbil holes. To improve the classification accuracy and reduce the time cost of repeated ‘trial and error’, we propose a novel method to build rule sets. We adopted the estimation of scale parameter_2 (ESP2) method during the segmentation stage to enable fast and objective segmentation parameterization. For the classification stage, a correlation-based feature selection (CFS) algorithm was applied for feature selection and threshold prediction. Our analysis determined the following universally recommended rule sets for identifying effective gerbil holes using OBIA: scale parameter of 107, shape factor of 0.2, compactness value of 0.3, excess green value less than −5, brightness feature value in the 143–149 range and roundness feature less than 0.9. With these rule sets, OBIA exhibited higher classification accuracy than maximum likelihood (ML) classification, with an overall classification accuracy of 88.25%. We also found that the relationship between the area of effective gerbil holes (y) and the grass coverage (x) satisfied a quadratic function: This study provides practical guidance for grassland management and rodent infestation control.