Spot Detection Algorithm Research Articles

Empowering photovoltaic power generation with edge computing: A recognition and location approach for hot spot

AbstractThe presented paper proposes a photovoltaic module hot spot detection algorithm based on YOLOv8‐BCB. The algorithm addresses issues such as component efficiency degradation and poor contact in long‐term operation of PV systems, with a focus on the hot spot effect’. To achieve rapid detection and localization of small targets in complex scenes, the algorithm incorporates the Weighted Bi‐directional Feature Pyramid Network (BiFPN) into YOLOv8. Additionally, a lightweight upsampling operator called Content‐Aware ReAssembly of FEatures (CARAFE) is used to reduce the detection load on the unmanned aerial vehicle (UAV) detection system. The BiFormer attention mechanism is also integrated into the C2f module to better capture dependencies between sequences and reduce background interference. To highlight the superiority of the authors’ algorithm compared to other high‐quality algorithms, the authors conducted comparative experiments on the same dataset. The YOLOv8‐BCB algorithm surpasses the SSD, faster‐cnn, and RetinaNet algorithms in both accuracy and detection speed. It achieves a precision of 97.1% and a recall rate of 94.9%, with an FPS of 110. The result is a target detection algorithm that is both fast and accurate.

Slope deformation detection using subpixel offset tracking and an unsupervised learning technique based on unmanned aerial vehicle photogrammetry data

Detecting slope deformation is an important issue in engineering. Timely deformation detection can effectively avoid catastrophic slope failure and ensure the safety of a project and engineering personnel. In this study, deformation detection for a quarry slope is implemented using the integration of subpixel offset tracking (sPOT) and unsupervised change detection algorithms based on unmanned aerial vehicle (UAV) image datasets. The sPOT algorithm is used to give the surface displacement field of the slope with subpixel accuracy, and the unsupervised change detection algorithm yields the ground object reconstruction area of the slope to verify and explain the sPOT result. The integrated analysis method in this paper is highly applicable and only requires a minimum of two UAV datasets as raw data. Combining the advantages of the sPOT and unsupervised change detection algorithms, the proposed method has the ability to detect and analyse slow and rapid slope deformation with good accuracy.

Design of Edge Computing System for Photovoltaic Panel Hot Spot Detection Based on Machine Learning

The hot spot effect of photovoltaic panel refers to the local heating phenomenon caused by the photovoltaic panel being covered, which not only seriously affects the power generation efficiency of photovoltaic panel, but also is one of the most important factors threatening the service life of photovoltaic panel. In this paper, an edge computing system was designed to detect hot spot effect based on real-time sensing data such as current, voltage and illuminance. The system consists of three parts: data acquisition side, data processing side and data display side. The hot spot detection algorithm model based on machine learning is deployed on the edge side, which can detect the degree of hot spot effect and locate the hot spot according to the sensor data of each photovoltaic panel in real time. Additionally, this system could push the data to the cloud management platform and each user terminal to realize remote operation and maintenance.

A Robust Phenotypic Screening Assay Utilizing Human Podocytes to Identify Agents that Modulate Lipid Droplets.

Lipid droplets (LDs), initially thought to be mere lipid storage structures, are highly dynamic organelles with complex functions that control cell fate and behavior. In recent years, their relevance as therapeutic targets for a wide array of human diseases has been well established. Consequently, efforts to develop tools to study them have intensified, including assays that can accurately track LD levels in clinically relevant cell-based models. We previously reported that LD accumulation destines podocytes for lipotoxicity and cell death in renal diseases of metabolic and nonmetabolic origin. We also showed that LD accumulation in those cells serves as both a marker for disease progression and as a therapeutic target. Here, we describe a robust phenotypic screening method, using differentiated human podocytes, for identifying small-molecule compounds that rescue podocytes from LD accumulation and lipotoxicity under cellular stress. Major assay advances include 1) the use of a solvatochromic dye to improve LD staining, reduce background noise, and improve detection accuracy, 2) use of confocal imaging to reduce vertical overlap of LDs and enable accurate counting, 3) combining membrane and cytoskeleton stains to improve cell segmentation in confocal mode, and 4) use of an optimized spot detection algorithm that requires minimal configuration per individual run. The assay is robust and yields a Z-factor that is consistently >0.5.

Basal Gp78-dependent mitophagy promotes mitochondrial health and limits mitochondrial ROS

Mitochondria are major sources of cytotoxic reactive oxygen species (ROS), such as superoxide and hydrogen peroxide, that when uncontrolled contribute to cancer progression. Maintaining a finely tuned, healthy mitochondrial population is essential for cellular homeostasis and survival. Mitophagy, the selective elimination of mitochondria by autophagy, monitors and maintains mitochondrial health and integrity, eliminating damaged ROS-producing mitochondria. However, mechanisms underlying mitophagic control of mitochondrial homeostasis under basal conditions remain poorly understood. E3 ubiquitin ligase Gp78 is an endoplasmic reticulum membrane protein that induces mitochondrial fission and mitophagy of depolarized mitochondria. Here, we report that CRISPR/Cas9 knockout of Gp78 in HT-1080 fibrosarcoma cells increased mitochondrial volume, elevated ROS production and rendered cells resistant to carbonyl cyanide m-chlorophenyl hydrazone (CCCP)-induced mitophagy. These effects were phenocopied by knockdown of the essential autophagy protein ATG5 in wild-type HT-1080 cells. Use of the mito-Keima mitophagy probe confirmed that Gp78 promoted both basal and damage-induced mitophagy. Application of a spot detection algorithm (SPECHT) to GFP-mRFP tandem fluorescent-tagged LC3 (tfLC3)-positive autophagosomes reported elevated autophagosomal maturation in wild-type HT-1080 cells relative to Gp78 knockout cells, predominantly in proximity to mitochondria. Mitophagy inhibition by either Gp78 knockout or ATG5 knockdown reduced mitochondrial potential and increased mitochondrial ROS. Live cell analysis of tfLC3 in HT-1080 cells showed the preferential association of autophagosomes with mitochondria of reduced potential. Xenograft tumors of HT-1080 knockout cells show increased labeling for mitochondria and the cell proliferation marker Ki67 and reduced labeling for the TUNEL cell death reporter. Basal Gp78-dependent mitophagic flux is, therefore, selectively associated with reduced potential mitochondria promoting maintenance of a healthy mitochondrial population, limiting ROS production and tumor cell proliferation.

Close-range Hot Spot Detection based on K-neighbor Algorithm and Experiment on Hot Spot Effect

Abnormalities caused by the hot spot effect are very common in the application of photovoltaic modules, which seriously affect the life and power generation efficiency of photovoltaic modules. Therefore, the study of hot spot effect is of great significance to the development of photovoltaic industry in the future. By analyzing the hot spot effect and its causes, this paper lists possible factors that may affect the hot spot effect, uses the K-proximity algorithm for hot spot detection to classify the near-field infrared images obtained in photovoltaic power plants, and designs experiments to explore the influence of several factors on the hot spot effect, the influence characteristics of the shading area and the types of shading objects are deeply explored, and finally a certain summary is made.

Fuzzy-Based Spatiotemporal Hot Spot Intensity and Propagation—An Application in Crime Analysis

Cluster-based hot spot detection is applied in many disciplines to analyze the locations, concentrations, and evolution over time for a phenomenon occurring in an area of study. The hot spots consist of areas within which the phenomenon is most present; by detecting and monitoring the presence of hot spots in different time steps, it is possible to study their evolution over time. One of the most prominent problems in hot spot analysis occurs when measuring the intensity of a phenomenon in terms of the presence and impact on an area of study and evaluating its evolution over time. In this research, we propose a hot spot analysis method based on a fuzzy cluster hot spot detection algorithm, which allows us to measure the incidence of hot spots in the area of study. We analyze its variation over time, and in order to evaluate its reliability we use a well-known fuzzy entropy measure that was recently applied to measure the reliability of hot spots by executing fuzzy clustering algorithms. We apply this method in crime analysis of the urban area of the City of London, using a dataset of criminal events that have occurred since 2011, published by the City of London Police. The obtained results show a decrease in the frequency of all types of criminal events over the entire area of study in recent years.

Study on Hot Spot Detection Algorithm based on K-Means

Solar energy is a widely used clean energy at present, and it is inexhaustible. As the main power generation device, photovoltaic modules play an important role in solar power generation. Among the various abnormalities of photovoltaic modules, the hot spot effect will not only reduce the power generation efficiency of photovoltaic modules, but even cause fires due to high temperatures. In this paper, k-means algorithm is adopted for photovoltaic hot spot detection. K-means is an unsupervised machine learning clustering algorithm, which clusters the pixel points of the image, and extracting the connected domain with the points with the shape similar to rectangular rectangle in the clustering, that is, obtaining the hot spot area. At the same time, this paper also uses the traditional computer vision MSER algorithm for experiments, compares the MSER algorithm with the K-means algorithm, and draws the conclusion that the traditional computer vision algorithm has low accuracy but high speed. Machine learning algorithms are accurate but fast.

Emergency Landing Spot Detection Algorithm for Unmanned Aerial Vehicles

The use and research of Unmanned Aerial Vehicle (UAV) have been increasing over the years due to the applicability in several operations such as search and rescue, delivery, surveillance, and others. Considering the increased presence of these vehicles in the airspace, it becomes necessary to reflect on the safety issues or failures that the UAVs may have and the appropriate action. Moreover, in many missions, the vehicle will not return to its original location. If it fails to arrive at the landing spot, it needs to have the onboard capability to estimate the best area to safely land. This paper addresses the scenario of detecting a safe landing spot during operation. The algorithm classifies the incoming Light Detection and Ranging (LiDAR) data and store the location of suitable areas. The developed method analyses geometric features on point cloud data and detects potential right spots. The algorithm uses the Principal Component Analysis (PCA) to find planes in point cloud clusters. The areas that have a slope less than a threshold are considered potential landing spots. These spots are evaluated regarding ground and vehicle conditions such as the distance to the UAV, the presence of obstacles, the area’s roughness, and the spot’s slope. Finally, the output of the algorithm is the optimum spot to land and can vary during operation. The proposed approach evaluates the algorithm in simulated scenarios and an experimental dataset presenting suitability to be applied in real-time operations.

DNEMO: a tool for quantification of mRNA and punctate structures in time-lapse images of single cells.

Many biological processes are regulated by single molecules and molecular assemblies within cells that are visible by microscopy as punctate features, often diffraction limited. Here, we present detecting-NEMO (dNEMO), a computational tool optimized for accurate and rapid measurement of fluorescent puncta in fixed-cell and time-lapse images. The spot detection algorithm uses the à trous wavelet transform, a computationally inexpensive method that is robust to imaging noise. By combining automated with manual spot curation in the user interface, fluorescent puncta can be carefully selected and measured against their local background to extract high-quality single-cell data. Integrated into the workflow are segmentation and spot-inspection tools that enable almost real-time interaction with images without time consuming pre-processing steps. Although the software is agnostic to the type of puncta imaged, we demonstrate dNEMO using smFISH to measure transcript numbers in single cells in addition to the transient formation of IKK/NEMO puncta from time-lapse images of cells exposed to inflammatory stimuli. We conclude that dNEMO is an ideal user interface for rapid and accurate measurement of fluorescent molecular assemblies in biological imaging data. The data and software are freely available online at https://github.com/recleelab. Supplementary data are available at Bioinformatics online.

Gas flaring activity and black carbon emissions in 2017 derived from the Sentinel-3A Sea and Land Surface Temperature Radiometer

Abstract. Gas flares are a regionally and globally significant source of atmospheric pollutants. They can be detected by satellite remote sensing. We calculate the global flared gas volume and black carbon emissions in 2017 by applying (1) a previously developed hot spot detection and characterisation algorithm to all observations of the Sea and Land Surface Temperature Radiometer (SLSTR) instrument on board the Copernicus satellite Sentinel-3A and (2) newly developed filters for identifying gas flares and corrections for calculating both flared gas volumes (billion cubic metres, BCM) and black carbon (BC) emissions (g). The filter to discriminate gas flares from other hot spots uses the observed hot spot characteristics in terms of temperature and persistence. A regression function is used to correct for the variability of detection opportunities. A total of 6232 flaring sites are identified worldwide. The best estimates of the annual flared gas volume and the BC emissions are 129 BCM with a confidence interval of [35, 419 BCM] and 73 Gg with a confidence interval of [20, 239 Gg], respectively. Comparison of our activity (i.e. BCM) results with those of the Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire data set and SWIR-based calculations show general agreement but distinct differences in several details. The calculation of black carbon emissions using our gas flaring data set with a newly developed dynamic assignment of emission factors lie in the range of recently published black carbon inventories, albeit towards the lower end. The data presented here can therefore be used e.g. in atmospheric dispersion simulations. The advantage of using our algorithm with Sentinel-3 data lies in the previously demonstrated ability to detect and quantify small flares, the long-term data availability from the Copernicus programme, and the increased detection opportunity of global gas flare monitoring when used in conjunction with the VIIRS instruments. The flaring activity and related black carbon emissions are available as “GFlaringS3” on the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) website (https://doi.org/10.25326/19, Caseiro and Kaiser, 2019).

Far-field laser spot image detection for use under atmospheric turbulence

In wireless laser communication, the effects of atmospheric turbulence cause laser spots to appear as irregular speckles in the far-field plane, which may make automatic acquisition, pointing, and tracking systems unable to locate a spot position. We propose an innovative and precise anti-interference, spot-detection algorithm for far-field irregular speckles based on the intensity characteristics of the far-field spots. Before obtaining the spot center coordinates through two-dimensional vector space circle fitting, the proposed algorithm employs computer graphics combined with mathematical morphology to fill the connected domains and extracts light-spot edges using the Canny operator, thus resulting in an accurate detection of far-field spots. The algorithm validation, using an automated experimental platform (atmospheric turbulent refractive-index structure parameter C n 2 is 10 − 15 m − 2/3), yields a detection threshold accuracy of 30 to 32 pixels (each pixel represents 3 μrad) for 10.2 km far-field spot images. The corresponding deviation satisfies the two-dimensional aiming platform for a minimum step distance of 17.45 μrad, verifying the feasibility and effectiveness of the algorithm.

High-Content Screening for Protein-Protein Interaction Modulators Using Proximity Ligation Assay in Primary Neurons.

The proximity ligation assay (PLA) allows the detection and subcellular localization of protein-protein interactions with high specificity. We recently developed a high-content screening model based on primary hippocampal neurons cultured in 384-well plates and screened a library of ∼1100 compounds using a PLA between tau and bridging integrator 1, a genetic risk factor for Alzheimer's disease. We developed image-segmentation and spot-detection algorithms to delineate PLA signals in the axonal network, but not in cell bodies, from confocal images acquired via a high-throughput microscope. To compare data generated from different plates and through different experiments, we developed a computational routine to optimize the image analysis parameters for each plate and devised a range of quality-control measures to ultimately identify compounds that consistently increase or decrease our read-out. We provide the following protocols. © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: Routine culture of rat postnatal hippocampal neurons in 384-well plates Basic Protocol 2: Compound incubation using the high-content screening platform Support Protocol 1: Preparation of intermediate plates for compound screening Support Protocol 2: Preparation of intermediate plates for hit validation (dose-response curves) Basic Protocol 3: Proximity ligation assay in 384-well plates Basic Protocol 4: Image acquisition and analysis Support Protocol 3: Optimization of analysis parameters Basic Protocol 5: Identification of hits Basic Protocol 6: Validation of hits based on dose-response curves.

BactMAP: An R package for integrating, analyzing and visualizing bacterial microscopy data.

High‐throughput analyses of single‐cell microscopy data are a critical tool within the field of bacterial cell biology. Several programs have been developed to specifically segment bacterial cells from phase‐contrast images. Together with spot and object detection algorithms, these programs offer powerful approaches to quantify observations from microscopy data, ranging from cell‐to‐cell genealogy to localization and movement of proteins. Most segmentation programs contain specific post‐processing and plotting options, but these options vary between programs and possibilities to optimize or alter the outputs are often limited. Therefore, we developed BactMAP (Bacterial toolbox for Microscopy Analysis & Plotting), a command‐line based R package that allows researchers to transform cell segmentation and spot detection data generated by different programs into various plots. Furthermore, BactMAP makes it possible to perform custom analyses and change the layout of the output. Because BactMAP works independently of segmentation and detection programs, inputs from different sources can be compared within the same analysis pipeline. BactMAP complies with standard practice in R which enables the use of advanced statistical analysis tools, and its graphic output is compatible with ggplot2, enabling adjustable plot graphics in every operating system. User feedback will be used to create a fully automated Graphical User Interface version of BactMAP in the future. Using BactMAP, we visualize key cell cycle parameters in Bacillus subtilis and Staphylococcus aureus, and demonstrate that the DNA replication forks in Streptococcus pneumoniae dissociate and associate before splitting of the cell, after the Z‐ring is formed at the new quarter positions. BactMAP is available from https://veeninglab.com/bactmap.

Research on APT Spot Detection Algorithm in Space Optical Communication

In space laser communication system, the coarse tracking accuracy of the APT system directly affects the field of view selection and accuracy of fine tracking system, and the accuracy of the selection of the spot center position directly affects the accuracy of the coarse tracking system. Therefore, the selection of reasonable spot detection algorithm is an important guarantee for the stable establishment of APT system. Centroid method, hough transform method, and circle fitting method are often used in the analysis of spot center and the realization of spot tracking. In order to improve the detection accuracy and anti-jamming performance, this paper compares and analyzes several commonly used spot detection methods, and obtains the applicable conditions of each algorithm.

Simulations of camera-based single-molecule fluorescence experiments.

Single-molecule microscopy has become a widely used technique in (bio)physics and (bio)chemistry. A popular implementation is single-molecule Förster Resonance Energy Transfer (smFRET), for which total internal reflection fluorescence microscopy is frequently combined with camera-based detection of surface-immobilized molecules. Camera-based smFRET experiments generate large and complex datasets and several methods for video processing and analysis have been reported. As these algorithms often address similar aspects in video analysis, there is a growing need for standardized comparison. Here, we present a Matlab-based software (MASH-FRET) that allows for the simulation of camera-based smFRET videos, yielding standardized data sets suitable for benchmarking video processing algorithms. The software permits to vary parameters that are relevant in cameras-based smFRET, such as video quality, and the properties of the system under study. Experimental noise is modeled taking into account photon statistics and camera noise. Finally, we survey how video test sets should be designed to evaluate currently available data analysis strategies in camera-based sm fluorescence experiments. We complement our study by pre-optimizing and evaluating spot detection algorithms using our simulated video test sets.

Improving the Efficiency and the Accuracy of 2D Gel Electrophoresis Spot Detection Using Graphics Processing Units

Background: Proteomics was built around two-dimensional (2D) gel electrophoresis. Accurately analyzing the images generated from 2D gel electrophoresis for spot detection is a timeconsuming process especially for high-resolution and large images. Objective: In this paper, we present an accurate GPU-accelerated software tool for the detection and quantification of protein spots in 2D gel electrophoresis images. Method: We adopt pixel-based approach that employs wavelet relational fuzzy C-means clustering and distance transform to detect and quantify the protein spots. This pixel-based spot detection approach is more accurate than the contour-based approaches; however it is compute-intensive. So, along with algorithmic optimizations, we present the mapping and optimization of the pixel-based spot detection algorithm onto graphics processing units (GPUs); including NVIDIA and AMD GPUs. Results: This approach is proved to exhibit better spot detection in quantitative comparisons with the commercial software tools. Specifically, it achieves a degree of improvement in F-measure of 21.237% and 11.716% on the average compared to Delta2D and Melanie, respectively. We carry out experiments on images of large size and high resolution for healthy and diseased samples. Our implementation has accomplished up to five orders of magnitude speedup compared to the single-threaded MATLAB implementation. Conclusion: We proposed an accurate and efficient tool for detecting and quantifying the protein spots in 2D Gel Electrophoresis images. Our tool outperforms commercial software tools in accuracy of detecting protein spots while achieving significantly better performance than single-threaded MATLAB implementation by utilizing the GPU accelerators. Keywords: Image processing, 2D gel electrophoresis, heterogeneous computing, parallel computing, GPU, OpenCL.

Blind spot monitoring at night-time using rear-view camera

The blind spot monitoring (BSM) is one of the key functions of advanced driver assistance system (ADAS). In this paper, we propose vision-based blind spot detection and tracking algorithm which is applicable at night-time. The feature of highly bright blobs due to the headlights of behind vehicle is employed for the detection of vehicles in blind spot area, because the appearance-based approach that can be used at daytime is not any more appropriate. We calculate the motion vectors of detected blobs to find an approaching vehicle and make headlight grouping to estimate its position by use of the projection map. We can successfully generate an alarm signal by detection and tracking the overtaking vehicle in blind spot area at night-time.

Blind spot monitoring at night-time using rear-view camera

The blind spot monitoring (BSM) is one of the key functions of advanced driver assistance system (ADAS). In this paper, we propose vision-based blind spot detection and tracking algorithm which is applicable at night-time. The feature of highly bright blobs due to the headlights of behind vehicle is employed for the detection of vehicles in blind spot area, because the appearance-based approach that can be used at daytime is not any more appropriate. We calculate the motion vectors of detected blobs to find an approaching vehicle and make headlight grouping to estimate its position by use of the projection map. We can successfully generate an alarm signal by detection and tracking the overtaking vehicle in blind spot area at night-time.

Measuring mRNA Decay in Budding Yeast Using Single Molecule FISH.

Cellular mRNA levels are determined by the rates of mRNA synthesis and mRNA decay. Typically, mRNA degradation kinetics are measured on a population of cells that are either chemically treated or genetically engineered to inhibit transcription. However, these manipulations can affect the mRNA decay process itself by inhibiting regulatory mechanisms that govern mRNA degradation, especially if they occur on short time-scales. Recently, single molecule fluorescent in situ hybridization (smFISH) approaches have been implemented to quantify mRNA decay rates in single, unperturbed cells. Here, we provide a step-by-step protocol that allows quantification of mRNA decay in single Saccharomyces cerevisiae using smFISH. Our approach relies on fluorescent labeling of single cytoplasmic mRNAs and nascent mRNAs found at active sites of transcription, coupled with mathematical modeling to derive mRNA half-lives. Commercially available, single-stranded smFISH DNA oligonucleotides (smFISH probes) are used to fluorescently label mRNAs followed by the quantification of cellular and nascent mRNAs using freely available spot detection algorithms. Our method enables quantification of mRNA decay of any mRNA in single, unperturbed yeast cells and can be implemented to quantify mRNA turnover in a variety of cell types as well as tissues.