Debris Detection Research Articles

Faster-PGYOLO: an efficient framework for floating debris detection in inland waters

Faster-PGYOLO: an efficient framework for floating debris detection in inland waters

Wide band high gain modified-folded-wave-guide slow wave structure for millimeter-wave traveling wave tube amplifier in 6G and beyond back-haul communications

Abstract Millimeter-wave technology is crucial in telecommunications as it enables higher bandwidths and faster data transfer. V-band represents frequencies ranging from 50 to 75 GHz and is used for short-range back-haul communication, defense radar, inter-satellite communications, and space debris detection. Folded waveguide slow-wave structures (FWG-SWS) are an excellent choice for V-band amplifiers due to their wide bandwidths, large gain, and ease of fabrication using modern lithographic processes. This article presents a modified folded waveguide slow-wave structure (MFW-SWS) for V-band traveling wave tube amplifiers (TWTAs) desirable for multi-gigabit 6G and beyond back-haul communications. The MFW slow-wave structure is an FWG-SWS variant that supports an increased RF phase velocity, accommodating additional SWS length for much-improved amplification. The dispersion analysis effectively provides a 15% operational bandwidth ranging from 64–74 GHz with a reduced phase velocity of three-tenths the speed of light apt for enhanced beam-wave interactions. The two-section SWS has independent attenuators designed to provide over 20 dB attenuation throughout the functional bandwidth effortlessly. Additional wave-guide transitions are introduced to achieve the best attenuation characteristics. Along the axis of the SWS, a square beam tunnel is employed to support a 26 kV round electron beam with a beam current of 70 mA. To contain the electron beam within the beam tunnel, this study used a focusing mechanism with a constant magnetic field of 0.25T, which is highly appropriate for miniaturization and large-scale implementation in 6G back-haul towers. Particle-in-cell (PIC) simulations were performed using the CST (Computer Simulation Technology) Studio Suite, and a gain of over 26 dB with an output power of 50 W was successfully attained across the operational bandwidth.

VA-YOLO: a foreign object debris detection method on the airport runway based on vision transformer and attention

VA-YOLO: a foreign object debris detection method on the airport runway based on vision transformer and attention

Optical Remote Sensing Methods for Floating Marine Debris Detection – Review and Bibliography Analysis

In the last several decades, the disposal and accumulation of debris in the marine and coastal environment has been one of the biggest and fastest-growing threats to the health of the world's oceans. Marine debris is present in all marine habitats and represents a great danger to global environmental and human health. Therefore, this paper aims to support efforts to achieve Good Environmental Status with respect to marine debris in all oceans and seas. The application of optical remote sensing methods for floating marine debris detection was briefly reviewed and the bibliometric analysis of the professional and scientific literature elaborated upon. In the overview part, previously used marine debris detection methods have been listed. The WoSCC (Web of Science Core Collection) and Scopus databases were considered, and the R Studio Bibliometrix and Biblioshiny software tools used for bibliometric analysis. 209 documents that can be classified into 43 research fields were identified in the two databases. Approximately 20% of the documents were published in two journals: the Marine Pollution Bulletin and Remote Sensing. Marine debris research was mainly published in the USA, Portugal, Italy, the United Kingdom, Germany, and China. A total of 54 countries participated in the publication of documents and it should be emphasized that all countries have shown great interest in international cooperation during the scientific research on marine debris. Scientific research on marine debris was found to have increased significantly since 2017, which is highly important for the protection of the environment and human health.

Faint Space Target Information Extraction Based on Small Aperture Telescope in Complex Background

AbstractThere are many problems in space debris monitoring with ground‐based telescopes, such as too many stars in the same field of view, uneven background and optical distortion in the optical system. We propose a two‐stage weak debris detection algorithm. In the first stage, wavelet transform is used to extract different components of three frames of images, and the median of corresponding components of the images is taken respectively to eliminate the influence of stars. In the second stage, an improved version of the faint space target extraction based on principal component analysis. The algorithm uses a smooth‐detection idea to extract target information. Based on a 150 mm aperture telescope, we improved the existing method of faint space debris extraction based on principal component analysis by introducing the smooth‐detection idea, and transformed the target detection problem into the separation problem of sparse matrix and low‐rank matrix. We applied a certain preprocessing consisting of wavelet‐based star removal and median pre‐filtering to keep as little noise and other contaminants as possible. After experimental measurements by observers, the algorithm demonstrated advanced detection capabilities on multiple indicators.

On Ion-Acoustic Precursor Soliton Signatures of Orbital Debris in Low Earth Orbit

Nonlinear plasma solitons have been recently proposed as a possible, indirect way to detect dangerous small orbital debris in low Earth orbit. A kinetic simulation study of the interaction between small debris flowing through a plasma at hypersonic velocity is presented, with a particular focus on whether the debris–plasma interaction leads to the formation of ion-acoustic precursor solitons. By using an electrostatic kinetic model and in the absence of an external magnetic field, simulation results where the debris is treated as a Gaussian density source are compared with those where the debris–plasma interaction is modeled self-consistently, in both one and two dimensions. While ion-acoustic solitons are formed when the debris is treated as a Gaussian density source and the ions are cold, it is shown that self-consistent debris charging with similar parameters hinders the formation of precursor solitons. This indicates that self-consistent debris charging is key to quantitative predictions of soliton formation. Additionally, when the ion and electron temperatures are comparable, as typical of conditions in low Earth orbit, Landau damping physics also hinders the formation of ion-acoustic precursor solitons. These results suggest that it will be challenging to exploit ion-acoustic precursor solitons for orbital debris detection in low Earth orbit.

Correction: A Comparative Study of Wear Debris and Vibration-Based Gear Damage Detection Methods Applied to Mild Wear in a Spur Gear System

Correction: A Comparative Study of Wear Debris and Vibration-Based Gear Damage Detection Methods Applied to Mild Wear in a Spur Gear System

A Comparative Study of Wear Debris and Vibration-Based Gear Damage Detection Methods Applied to Mild Wear in a Spur Gear System

A Comparative Study of Wear Debris and Vibration-Based Gear Damage Detection Methods Applied to Mild Wear in a Spur Gear System

A Dataset for Detection and Segmentation of Underwater Marine Debris in Shallow Waters

Robust object detection is crucial for automating underwater marine debris collection. While supervised deep learning achieves state-of-the-art performance in discriminative tasks, replicating this success on underwater data is challenging. The generalization of these methods suffers due to a lack of available annotated data considering different sources of variation in the unstructured underwater environment and imaging conditions. In this paper, we present the Seaclear Marine Debris Dataset, the first publicly available shallow-water marine debris dataset annotated for instance segmentation/object detection. The dataset contains 8610 images collected using ROVs at multiple locations and with different cameras, annotated for 40 object categories, encompassing not only litter but also observed animals, plants, and robot parts. As part of the technical validation, we provide baseline results for object detection using Faster RCNN and YOLOv6 models. Furthermore, we demonstrate the non-triviality of generalizing the trained model performance to unseen sites and cameras due to domain shift. This underscores the value of the presented dataset in further developing robust models for underwater debris detection.

Significance of scattered small echogenic foci floating in urinary bladder as ultrasonography finding in dogs

BackgroundDespite the prevalence of echogenic foci floating in the urinary bladder seen in ultrasonography in dogs, surprisingly little has been written on its significance, including its potential association with urinalysis. The objective of the study was to determine the diagnostic value of the echogenic foci floating in urinary bladders in dogs.Results- Cystosonography was performed on 45 dogs. Bladder contents were examined and divided into positive (containing echogenic particles) and negative (absent echogenic particles) groups according to the presence and absence of floating echogenic particles. Five mL of urine was collected via cystocentesis. Urine analysis and culture were done and the relationship between ultrasound evaluation and urinalysis results was investigated. In dogs with bladder echogenic particles in ultrasonography, the prevalence of hematuria, pyuria, bacteriuria, and lipiduria were 88.9%, 92.6%, 29.6%, and 70.3%, respectively. However, in dogs in which echogenic particles were not observed in their bladders, the prevalence of hematuria, pyuria, bacteriuria, and lipiduria was 77%, 50%, 5.5%, and 77%, respectively. There was a significant association between bladder debris and positive urine culture, with an odds ratio of 7.15 (95% confidence interval: 0.81–63.28) compared with matched controls. Furthermore, there was a significant relationship between the presence of floating echogenic particles with pyuria, and urine color ( p ≤ 0.05).ConclusionIn conclusion, the present results showed the detection of bladder debris on ultrasound can be a predictor for pyuria and positive urine culture in dogs.

In-Line Thermal Desorption and Dielectric Barrier Discharge Ionization for Rapid Mass Spectrometry Detection of Explosives.

Thermal desorption (TD) of wipe-based samples was coupled with an in-line dielectric barrier discharge ionization (DBDI) source and rugged compact time-of-flight mass spectrometer (MS) for the detection of explosives, propellants, and postblast debris. The chromatography-free TD-DBDI-MS platform enabled rapid and sensitive detection of organic nitramine, nitrate ester, and nitroaromatic explosives as well as black powder and black powder substitute propellants. Parametric investigations characterized the response to TD temperature and optimized DBDI voltage, aerodynamically assisted entrainment, and fragmentation through in-source collision induced dissociation (isCID). Excess nitrate generated by the DBDI source yielded predominantly nitrate-adduct formation. Subnanogram sensitivities were demonstrated for all explosives investigated, except for nitroglycerin, specifically due to its volatility. Further, most analytes/explosives exhibited tens of picograms sensitivities. The platform also demonstrated the detection of propellant and military explosives from postblast debris. The TD-DBDI-MS system performed well without the need for aerodynamically assisted entrainment (and the associated rough pump), which along with requiring no additional gases (i.e., N2 or He) or solvents, aid in potential field deployment. The ease of TD-DBDI attachment and removal added trace solid or liquid residue detection to the rugged mass spectrometer, designed primarily for the analysis of volatile organic and inorganic compounds.

Foreign Object Debris Detection on Wireless Electric Vehicle Charging Pad Using Machine Learning Approach

Foreign object debris (FOD) includes any unwanted and unintentional material lying on the charging lane or parking lots, posing a risk to the wireless charging system, the vehicle, or the people inside. FOD in an Electric Vehicle (EV) wireless charging system can cause problems, including decreased charging efficiency, safety risks, charging system damage, communication issues, and health risks. To address this problem, this paper proposes the deep learning object detection network approach of using YOLOv4 (You Only Look Once), which is a single-shot detector. Additionally, for real-time implementation, YOLOv4-Tiny is suggested, which is a compressed version of YOLOv4 designed for devices with low computational power. YOLOv4-Tiny enables faster inferences and facilitates the deployment of FOD detectors on edge devices. The algorithm is trained using the FOD dataset, consisting of images of common debris on runways or taxiways. Furthermore, utilizing the concept of transfer learning, the last few layers of the pre-trained YOLOv4 model are modified using the COCO (Common Objects in Context) dataset to transfer features to the new network and retrain the model on the FOD dataset. The results obtained using this YOLOv4 model yielded a precision rate of 99.05%, while the results from YOLOv4-Tiny achieved a precision rate of 97.74%, with an average inference time of 150 ms under the ambient light and weather conditions.

Acoustic camera-based super-resolution reconstruction approach for underwater perception in low-visibility marine environments

In low-visibility environments, the underwater perception range of optical cameras is severely restricted, and perception operations in ocean engineering often rely on sonar. Acoustic cameras are a type of forward-looking sonar that have attracted considerable attention because of their ability to produce images similar to those of optical cameras. However, owing to the unique imaging mechanism employed by acoustic cameras, the resulting images suffer from insufficient resolution and a loss of feature details. This issue considerably diminishes the precision of downstream visual tasks, limiting the application of acoustic cameras. In this study, we propose a deep-learning-based super-resolution reconstruction approach for acoustic cameras, where the reconstruction process relies only on images, without prior assumptions regarding the detection scenes. We verified the effectiveness of the proposed method for two practical applications: marine debris detection and marine structure inspection. The experimental results show that our proposed method can robustly reconstruct high-resolution sonar images, and the obtained images have superior feature details, which improved the precision of downstream vision tasks. In this study, we aim to provide better solutions for underwater perception in low-visibility marine environments, while exploring the application of acoustic cameras in marine debris detection and structure inspection.

Improving Estimation of Specific Differential Phase and Assessing Differential Backscattering Phase

Abstract Polarimetric variables such as differential phase ΦDP and its range derivative, specific differential phase KDP, contain useful information for improving quantitative precipitation estimation (QPE) and microphysics retrieval. However, the usefulness of the current operationally utilized estimation method of KDP is limited by measurement error and artifacts resulting from the differential backscattering phase δ. The contribution of δ can significantly influence the ΦDP measurements and therefore negatively affect the KDP estimates. Neglecting the presence of δ within non-Rayleigh scattering regimes has also led to the adoption of incorrect terminology regarding signatures seen within current operational KDP estimates implying associated regions of unrealistic liquid water content. A new processing method is proposed and developed to estimate both KDP and δ using classification and linear programming (LP) to reduce bias in KDP estimates caused by the δ component. It is shown that by applying the LP technique specifically to the rain regions of Rayleigh scattering along a radial profile, accurate estimates of differential propagation phase, specific differential phase, and differential backscattering phase can be retrieved within regions of both Rayleigh and non-Rayleigh scattering. This new estimation method is applied to cases of reported hail and tornado debris, and the LP results are compared to the operationally utilized least squares fit (LSF) estimates. The results show the potential use of the differential backscattering phase signature in the detection of hail and tornado debris.

Exploring Deep Learning for Underwater Plastic Debris Detection and Monitoring

Exploring Deep Learning for Underwater Plastic Debris Detection and Monitoring

A Wear Debris Signal Processing Method Based on Inductive Monitoring for Aero-Engine

In view of the high false alarm rate in the oil debris monitoring results of the triple-coil inductive sensor in the transmission lubrication system of the aero-engine, a new debris signal processing method based on inductive monitoring is proposed. A time domain analysis is carried out first, and the signal energy is the most effective index to distinguish the debris signature from the noise signature. On this basis, signal energy values within a fixed-length sliding window is processed through the histogram. Finally, a threshold is set for the detection of the debris signature, which is based on the distribution of data within the histogram. This method is applied to the experimental data from a test run of an aero-engine, and the results show that all the debris is detected even if part of it appears during a change in the working condition of the aero-engine. Therefore, this method shows satisfactory results in debris detection accuracy and especially the inhibition of false alarms. It is also applicable for real-time monitoring due to the similarity between the movement of the sliding window and real-time data acquisition. In addition, it is applicable for various sensing principles, including but not limited to the inductive sensor signal, since the detection performance is only related to the signal itself.

Enhanced floating debris detection algorithm based on CDW-YOLOv8

The issue of floating debris on water surfaces is becoming increasingly prominent, posing significant threats to aquatic ecosystems and human habitats. The detection of floating debris is impeded by complex backgrounds and water currents, resulting in suboptimal detection accuracy. To enhance detection effectiveness, this study presents a floating debris detection algorithm rooted in CDW-YOLOv8. Firstly, the study augments the original C2f module by incorporating the Coordinate Attention (CA) mechanism, resulting in the C2f-CA module, to boost the model’s sensitivity to target locations. Secondly, the study substitutes the standard Upsample module with the DySample module to diminish model parameters and increase flexibility. Furthermore, the study incorporates a small object detection layer to enhance the detection performance of small floating debris. Lastly, the Complete-IOU (CIOU) loss function is substituted by the Focaler-Wise-IOU v3 (Focaler-WIoUv3) loss function, which aims to minimize the impact of low-quality anchor boxes and improve regression accuracy. Experimental results demonstrate that the improved CDW-YOLOv8 algorithm has realized a comprehensive performance improvement in accuracy, recall rate, mAP@0.5, and mAP@0.5:0.95, noting increases of 2.9%, 0.6%, 2.5%, and 1.5%, respectively, relative to the original YOLOv8 algorithm. This offers a robust reference for the intelligent detection and identification of floating debris on water surfaces.

Oil wear debris sensor with temperature compensation

In this paper, in response to the problem that oil temperature changes will affect the detection results of the inductive wear debris sensor, a Wheatstone bridge-type oil wear debris sensor was designed using two two-wire helical tube coils, precision potentiometers, precision resistors, and a conditioning circuit with differential, phase-locked, and amplification functions were designed. Through practical tests, 72–400 μm iron particles were detected under the water bath's heating condition from 20℃ to 70℃. It proves that the designed Wheatstone bridge sensor and conditioning circuit can detect ferromagnetic particles in multiple temperature oil. The de-signed sensor and conditioning circuit effectively solves the problem of temperature drift of the detected signal under high-temperature conditions, improving the signal-to-noise ratio of wear debris detection under multiple temperatures. The sensor is suitable for lubricant detection systems with varying or constant high oil temperatures. The work presented in this paper provides significant reference value for the practical operation of oil sensors in real-world environments.

Charging of space debris in the LEO and GEO regions

We present theoretical estimates of the electrical charge and electrostatic surface potentials on debris objects orbiting in the LEO and GEO regions of the Earth's ionosphere. The estimates are obtained using analytic calculations based on an improved Orbital Motion Limited model as well as through numerical Particle-In-Cell simulations using the open-source SPIS code. A variety of shapes, sizes, and material compositions of the debris objects are considered, including HAMR objects with a high area-to-mass ratio and objects composed of insulating and conducting material patches. In the GEO region, we consider the charging arising from photo-emission effects and the impact of energetic charged particle beams associated with solar flares. We discuss the nature of the debris-induced collective excitations in the plasma and their potential use for debris detection.

A Semi-Supervised Object Detection Method for Close Range Detection of Spacecraft and Space Debris

A Semi-Supervised Object Detection Method for Close Range Detection of Spacecraft and Space Debris