Automated Analysis Techniques Research Articles

Morphology of the normative human cone photoreceptor mosaic and a publicly available adaptive optics montage repository.

Adaptive optics ophthalmoscopy has enabled visualization of the in vivo human photoreceptor mosaic in health, disease and its treatment. Despite this, the clinical utility of the imaging technology has been limited by a lack of automated analysis techniques capable of accurately quantifying photoreceptor structure and a lack of an available normative image database. Here, we present a fully automated algorithm for estimating cone spacing and density over a complete adaptive optics montage along with a database of normative images and cone densities. We imaged the cone mosaics surrounding the fovea and along the horizontal and vertical meridians of fifty normal-sighted controls with a custom-built, multimodal adaptive optics scanning light ophthalmoscope. Cone spacing was automatically measured in the frequency domain and spacing measurements were converted to estimates of cone density at all locations across the montage. Consistent with previous reports, cone density measurements were highest near fovea (152,906 ± 53,209 cones/mm2) and decreased exponentially with eccentricity. A 2.5-fold variation was found in cone density estimates at 0.1mm, this variation decreased to 1.75-fold at 1mm. We provide all images, mosaic quantifications, and automated software open source. This database will aid investigators in translating adaptive optics ophthalmoscopy to clinical applications.

Role of pause duration in primary progressive aphasia

ABSTRACT Aims To detect differences in speech fluency in separate primary progressive aphasia syndromes (PPA) using automated analysis techniques. The resulting linguistic features are evaluated for their use in a predictive model to identify common patterns in speakers with PPA. As fluency is observable in audio recordings, its quantification may provide a low-cost instrument that augments spontaneous speech analyses in clinical practice. Methods and Procedures Speech was recorded in 14 controls, 7 nonfluent variant (nfvPPA) and 8 semantic variant (svPPA) speakers. The recordings were annotated for speech and non-speech with Kaldi, a common toolkit for speech processing software. Variables relating to fluency (pause rate, number of pauses, length of pauses) were analyzed. Outcomes and Results The best fitting distribution of pause duration was a combination of two Gaussian distributions, corresponding with pause categories short vs. long. Group level differences were found in the rate of pauses and proportion of silence: nfvPPA speakers use more short pauses relative to long pauses than control speakers, and the duration of short and long pauses is longer; svPPA speakers use more longer pauses relative to short pauses. Their short pauses are significantly shorter than those from control speakers. Participants in both PPA groups pause more frequently. SvPPA speakers are typically perceived as fluent. However, our analysis shows their fluency patterns to be distinct from control speakers, if the long-short distinction is observed. Conclusions Automatic measurements of pause duration show meaningful distinctions across the groups and might provide future aid in clinical assessment.

Historic Built Environment Assessment and Management by Deep Learning Techniques: A Scoping Review

Recent advancements in digital technologies and automated analysis techniques applied to Historic Built Environment (HBE) demonstrate significant advantages in efficiently collecting and interpreting data for building conservation activities. Integrating digital image processing through Artificial Intelligence approaches further streamlines data analysis for diagnostic assessments. In this context, this paper presents a scoping review based on Scopus and Web of Science databases, following the PRISMA protocol, focusing on applying Deep Learning (DL) architectures for image-based classification of decay phenomena in the HBE, aiming to explore potential implementations in decision support system. From the literature screening process, 29 selected articles were analyzed according to methods for identifying buildings’ surface deterioration, cracks, and post-disaster damage at a district scale, with a particular focus on the innovative DL architectures developed, the accuracy of results obtained, and the classification methods adopted to understand limitations and strengths. The results highlight current research trends and the potential of DL approaches for diagnostic purposes in the built heritage conservation field, evaluating methods and tools for data acquisition and real-time monitoring, and emphasizing the advantages of implementing the adopted techniques in interoperable environments for information sharing among stakeholders. Future challenges involve implementing DL models in mobile apps, using sensors and IoT systems for on-site defect detection and long-term monitoring, integrating multimodal data from non-destructive inspection techniques, and establishing direct connections between data, intervention strategies, timing, and costs, thereby improving heritage diagnosis and management practices.

Deep learning in marine bioacoustics: a benchmark for baleen whale detection

AbstractPassive acoustic monitoring (PAM) is commonly used to obtain year‐round continuous data on marine soundscapes harboring valuable information on species distributions or ecosystem dynamics. This continuously increasing amount of data requires highly efficient automated analysis techniques in order to exploit the full potential of the available data. Here, we propose a benchmark, which consists of a public dataset, a well‐defined task and evaluation procedure to develop and test automated analysis techniques. This benchmark focuses on the special case of detecting animal vocalizations in a real‐world dataset from the marine realm. We believe that such a benchmark is necessary to monitor the progress in the development of new detection algorithms in the field of marine bioacoustics. We ultimately use the proposed benchmark to test three detection approaches, namely ANIMAL‐SPOT, Koogu and a simple custom sequential convolutional neural network (CNN), and report performances. We report the performance of the three detection approaches in a blocked cross‐validation fashion with 11 site‐year blocks for a multi‐species detection scenario in a large marine passive acoustic dataset. Performance was measured with three simple metrics (i.e., true classification rate, noise misclassification rate and call misclassification rate) and one combined fitness metric, which allocates more weight to the minimization of false positives created by noise. Overall, ANIMAL‐SPOT performed the best with an average F metric of 0.83, followed by the custom CNN with an average fitness metric of 0.79 and finally Koogu with an average fitness metric of 0.59. The presented benchmark is an important step to advance in the automatic processing of the continuously growing amount of PAM data that are collected throughout the world's oceans. To ultimately achieve usability of developed algorithms, the focus of future work should be laid on the reduction of the false positives created by noise.

Mitigating the curse of dimensionality using feature projection techniques on electroencephalography datasets: an empirical review

Electroencephalography (EEG) is commonly employed to diagnose and monitor brain disorders, however, manual analysis is time-consuming. Hence, researchers nowadays are increasingly leveraging artificial intelligence (AI) techniques for automatic analysis of EEG, involving task-specific feature extraction and classification. Extracting multiple non-linear features from multiple EEG channels enhances the performance of classification, but it also results in high-dimensional features. However, the presence of the "curse of dimensionality" poses challenges for classifiers in AI-based EEG analyzers, leading to overfitting and complexity in classification. Therefore, in this empirical review, the effectiveness of the existing dimensionality reduction techniques to mitigate the curse of dimensionality in EEG feature sets is analyzed. We begin this study, by overviewing the extracted high-dimensional features from EEG signals related to disorders such as schizophrenia, alcoholism, focal seizures, focal seizures with deep features, and depression. Subsequently, 23 reduction techniques were reviewed, which transform the high-dimensional EEG features into a new reduced feature space to improve the classification. The impact of the reduced features was evaluated using traditional AI-based classifiers (support vector machine (SVM) and k-nearest neighbor (k-NN)). 10-fold cross-validation was performed for training and testing, and the performance was evaluated using accuracy, selected features, and dimensionality reduction rate (DRR) metrics. Comprehensive analysis of projection techniques on diverse EEG datasets offers valuable insights to assist researchers in selecting the most suitable technique. The hybrid projection technique termed principal component analysis-based t-distributed stochastic neighbor embedding (PCA+t-SNE) achieved an impressive average accuracy of 93.36%, surpassing the k-NN classifier without reduction techniques.

Muscle geometry and its relevance for sports performance? A perspective of current findings and future opportunities

Muscle geometry and its relevance for sports performance? A perspective of current findings and future opportunities

Informatization Integration Strategy of Modern Popular Music Teaching and Traditional Music Culture in Colleges and Universities in the Era of Artificial Intelligence

Abstract In this paper, a music teaching aid is constructed based on the automatic analysis technique of music elements. The auxiliary tool uses constant Q transform and note onset detection technology to process and recognize students’ input audio. The logical operation of music longitudinal structure and spectral structure and the logical conversion of beat and rhythmic time nodes are used to realize the integration of popular music and traditional music teaching informationization. Using two corpora of 22 kHz, the effect of teaching the fusion of musical elements of auxiliary tools was analyzed with the regular transformation of curves in the sound intensity envelope. The results showed that the peaks of the overlap between elements of popular music and traditional music appeared at 56.8 FPs, 148.9 FPs, and 343.7 FPs, respectively, corresponding to the peaks of 48.3 Hz, 59.4 Hz, and 51.6 Hz, respectively. The integration of traditional music culture into popular music teaching was successfully realized. This study effectively examined an effective method and strategy for integrating teaching, which has a positive impact on the promotion and development of traditional music culture.

Effects of Ransomware: Analysis, Challenges and Future Perspective

This review paper highlights the challenges and best practices in malware analysis, specifically focusing on the age of ransomware. It provides an overview of malware and its impact on computer systems and user privacy by lists various types of malware, including viruses, Trojans, spyware,adware, worms and highlights major malware attacks including the methods used and the resulting damages. Further, the article explores the challenges faced in ransomware analysis, including advanced encryption and evasion techniques, anti-analysis mechanisms, zero-day exploits and vulnerabilities, polymorphic and dynamic behavior, lack of resources, complexity of ransomware, collaboration difficulties, and cost implications. These challenges make it necessary for security researchers to constantly update their knowledge and techniques to effectively analyze ransomware. This study concludes best practices for ransomware analysis including isolating and segmenting ransomware samples in controlled environments, emphasizing behavior analysis and threat hunting, investing in advanced reverse engineering and automated analysis techniques, promoting collaborative intelligence and information sharing, and implementing security measures to protect against ransomware attacks. Additionally, the article briefly mentions static analysis techniques which explains that static analysis involves examining malware files and code without executing them. It can be used to identify ransomware characteristics, such as encryption algorithms, ransom demands, remote command execution, and obfuscation techniques. Moreover, file and code analysis methods, signature-based detection, code deobfuscation and unpacking techniques, and malicious document analysis and exploit detection are also suggested as part of static analysis.

Representing uncertainty through sentiment and stance visualizations: A survey

Visual analytics combines automated analysis techniques with interactive visualizations for effective understanding, reasoning, and decision-making on complex data. However, accurately classifying sentiments and stances in sentiment analysis remains challenging due to ambiguity and individual differences. This survey examines 35 papers published between 2016 and 2022, identifying unaddressed sources of friction that contribute to a gap between individual sentiment, processed data, and visual representation. We explore the impact of visualizations on data perception, analyze existing techniques, and investigate the many facets of uncertainty in sentiment and stance visualizations. We also discuss the evaluation methods used and present opportunities for future research. Our work addresses a gap in previous surveys by focusing on uncertainty and the visualization of sentiment and stance, providing valuable insights for researchers in graphical models, computational methods, and information visualization.

Improved Workflow for Analysis of Vascular Myocyte Time-Series and Line-Scan Ca2+ Imaging Datasets.

Intracellular Ca2+ signals are key for the regulation of cellular processes ranging from myocyte contraction, hormonal secretion, neural transmission, cellular metabolism, transcriptional regulation, and cell proliferation. Measurement of cellular Ca2+ is routinely performed using fluorescence microscopy with biological indicators. Analysis of deterministic signals is reasonably straightforward as relevant data can be discriminated based on the timing of cellular responses. However, analysis of stochastic, slower oscillatory events, as well as rapid subcellular Ca2+ responses, takes considerable time and effort which often includes visual analysis by trained investigators, especially when studying signals arising from cells embedded in complex tissues. The purpose of the current study was to determine if full-frame time-series and line-scan image analysis workflow of Fluo-4 generated Ca2+ fluorescence data from vascular myocytes could be automated without introducing errors. This evaluation was addressed by re-analyzing a published "gold standard" full-frame time-series dataset through visual analysis of Ca2+ signals from recordings made in pulmonary arterial myocytes of en face arterial preparations. We applied a combination of data driven and statistical approaches with comparisons to our published data to assess the fidelity of the various approaches. Regions of interest with Ca2+ oscillations were detected automatically post hoc using the LCPro plug-in for ImageJ. Oscillatory signals were separated based on event durations between 4 and 40 s. These data were filtered based on cutoffs obtained from multiple methods and compared to the published manually curated "gold standard" dataset. Subcellular focal and rapid Ca2+ "spark" events from line-scan recordings were examined using SparkLab 5.8, which is a custom automated detection and analysis program. After filtering, the number of true positives, false positives, and false negatives were calculated through comparisons to visually derived "gold standard" datasets. Positive predictive value, sensitivity, and false discovery rates were calculated. There were very few significant differences between the automated and manually curated results with respect to quality of the oscillatory and Ca2+ spark events, and there were no systematic biases in the data curation or filtering techniques. The lack of statistical difference in event quality between manual data curation and statistically derived critical cutoff techniques leads us to believe that automated analysis techniques can be reliably used to analyze spatial and temporal aspects to Ca2+ imaging data, which will improve experiment workflow.

Scaling whale monitoring using deep learning: A human-in-the-loop solution for analyzing aerial datasets

To ensure effective cetacean management and conservation policies, it is necessary to collect and rigorously analyze data about these populations. Remote sensing allows the acquisition of images over large observation areas, but due to the lack of reliable automatic analysis techniques, biologists usually analyze all images by hand. In this paper, we propose a human-in-the-loop approach to couple the power of deep learning-based automation with the expertise of biologists to develop a reliable artificial intelligence assisted annotation tool for cetacean monitoring. We tested this approach to analyze a dataset of 5334 aerial images acquired in 2017 by Fisheries and Oceans Canada to monitor belugas (Delphinapterus leucas) from the threatened Cumberland Sound population in Clearwater Fjord, Canada. First, we used a test subset of photographs to compare predictions obtained by the fine-tuned model to manual annotations made by three observers, expert marine mammal biologists. With only 100 annotated images for training, the model obtained between 90% and 91.4% mutual agreement with the three observers, exceeding the minimum inter-observer agreement of 88.6% obtained between the experts themselves. Second, this model was applied to the full dataset. The predictions were then verified by an observer and compared to annotations made completely manually and independently by another observer. The annotating observer and the human-in-the-loop pipeline detected 4051 belugas in common, out of a total of 4572 detections for the observer and 4298 for our pipeline. This experiment shows that the proposed human-in-the-loop approach is suitable for processing novel aerial datasets for beluga counting and can be used to scale cetacean monitoring. It also highlights that human observers, even experienced ones, have varied detection bias, underlining the need to discuss standardization of annotation protocols.

Automated detection of dolphin whistles with convolutional networks and transfer learning.

Effective conservation of maritime environments and wildlife management of endangered species require the implementation of efficient, accurate and scalable solutions for environmental monitoring. Ecoacoustics offers the advantages of non-invasive, long-duration sampling of environmental sounds and has the potential to become the reference tool for biodiversity surveying. However, the analysis and interpretation of acoustic data is a time-consuming process that often requires a great amount of human supervision. This issue might be tackled by exploiting modern techniques for automatic audio signal analysis, which have recently achieved impressive performance thanks to the advances in deep learning research. In this paper we show that convolutional neural networks can indeed significantly outperform traditional automatic methods in a challenging detection task: identification of dolphin whistles from underwater audio recordings. The proposed system can detect signals even in the presence of ambient noise, at the same time consistently reducing the likelihood of producing false positives and false negatives. Our results further support the adoption of artificial intelligence technology to improve the automatic monitoring of marine ecosystems.

Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images.

The number of coronavirus disease (COVID-19) cases is constantly rising as the pandemic continues, with new variants constantly emerging. Therefore, to prevent the virus from spreading, coronavirus cases must be diagnosed as soon as possible. The COVID-19 pandemic has had a devastating impact on people's health and the economy worldwide. For COVID-19 detection, reverse transcription-polymerase chain reaction testing is the benchmark. However, this test takes a long time and necessitates a lot of laboratory resources. A new trend is emerging to address these limitations regarding the use of machine learning and deep learning techniques for automatic analysis, as these can attain high diagnosis results, especially by using medical imaging techniques. However, a key question arises whether a chest computed tomography scan or chest X-ray can be used for COVID-19 detection. A total of 17,599 images were examined in this work to develop the models used to classify the occurrence of COVID-19 infection, while four different classifiers were studied. These are the convolutional neural network (proposed architecture (named, SCovNet) and Resnet18), support vector machine, and logistic regression. Out of all four models, the proposed SCoVNet architecture reached the best performance with an accuracy of almost 99% and 98% on chest computed tomography scan images and chest X-ray images, respectively.

Deep Learning for Skin Melanocytic Tumors in Whole-Slide Images: A Systematic Review.

The rise of Artificial Intelligence (AI) has shown promising performance as a support tool in clinical pathology workflows. In addition to the well-known interobserver variability between dermatopathologists, melanomas present a significant challenge in their histological interpretation. This study aims to analyze all previously published studies on whole-slide images of melanocytic tumors that rely on deep learning techniques for automatic image analysis. Embase, Pubmed, Web of Science, and Virtual Health Library were used to search for relevant studies for the systematic review, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Articles from 2015 to July 2022 were included, with an emphasis placed on the used artificial intelligence methods. Twenty-eight studies that fulfilled the inclusion criteria were grouped into four groups based on their clinical objectives, including pathologists versus deep learning models (n = 10), diagnostic prediction (n = 7); prognosis (n = 5), and histological features (n = 6). These were then analyzed to draw conclusions on the general parameters and conditions of AI in pathology, as well as the necessary factors for better performance in real scenarios.

A global ocean dissolved organic phosphorus concentration database (DOPv2021)

Dissolved organic phosphorus (DOP) concentration distributions in the global surface ocean inform our understanding of marine biogeochemical processes such as nitrogen fixation and primary production. The spatial distribution of DOP concentrations in the surface ocean reflect production by primary producers and consumption as an organic nutrient by phytoplankton including diazotrophs and other microbes, as well as other loss processes such as photolysis. Compared to dissolved organic carbon and nitrogen, however, relatively few marine DOP concentration measurements have been made, largely due to the lack of automated analysis techniques. Here we present a database of marine DOP concentration measurements (DOPv2021) that includes new (n = 730) and previously published (n = 3140) observations made over the last ~30 years (1990–2021), including 1751 observations in the upper 50 m. This dataset encompasses observations from all major ocean basins including the poorly represented Indian, South Pacific, and Southern Oceans and provides insight into spatial distributions of DOP in the ocean. It is also valuable for researchers who work on marine primary production and nitrogen fixation.

Corneal Nerve Changes Observed by In Vivo Confocal Microscopy in Patients Receiving Oxaliplatin for Colorectal Cancer: The COCO Study.

An objective method of early identification of people at risk of chemotherapy-induced peripheral neuropathy is needed to minimize long-term toxicity and maximize dose intensity. The aims of the study were to observe corneal nerve microstructure and corneal sensitivity changes and peripheral neuropathy in patients receiving oxaliplatin, and to determine its association with corneal parameters at different stages of treatment and assess utility as non-invasive markers to detect and monitor peripheral neuropathy. Twenty-three patients scheduled to receive oxaliplatin chemotherapy with intravenous 5-FU for gastro-intestinal cancer were recruited and followed up with for 12 months. Ocular examinations including corneal and retinal evaluations, alongside peripheral neuropathy assessment, were performed. The corneal nerve density did not show significant change after chemotherapy when measured with a widely used semi-automated program or an automated analysis technique. Macula and optic nerve function did not change during or after oxaliplatin chemotherapy. However, the corneal nerve density modestly correlated with clinical peripheral neuropathy after 20 weeks of chemotherapy (r = 0.61, p = 0.01) when peripheral neuropathy is typical most profound, and corneal nerve sensitivity correlated with neuropathy at 12 (r = 0.55, p = 0.01) and 20 weeks (r = 0.64, p = 0.006). In conclusion, corneal changes detected on confocal microscopy show moderate association with peripheral neuropathy, indicating their potential to identify the development of oxaliplatin-induced peripheral neuropathy. However, further studies are required to confirm these findings.

Automated pharyngeal phase detection and bolus localization in videofluoroscopic swallowing study: Killing two birds with one stone?

Background and objectiveThe videofluoroscopic swallowing study (VFSS) is a gold-standard imaging technique for assessing swallowing, but analysis and rating of VFSS recordings is time consuming and requires specialized training and expertise. Researchers have recently demonstrated that it is possible to automatically detect the pharyngeal phase of swallowing and to localize the bolus in VFSS recordings via computer vision approaches, fostering the development of novel techniques for automatic VFSS analysis. However, training of algorithms to perform these tasks requires large amounts of annotated data that are seldom available. In this paper, we demonstrate that the challenges of pharyngeal phase detection and bolus localization can be solved together using a single approach. MethodsWe propose a deep-learning framework that jointly tackles pharyngeal phase detection and bolus localization in a weakly-supervised manner, requiring only the initial and final frames of the pharyngeal phase as ground truth annotations for the training. Our approach stems from the observation that bolus presence in the pharynx is the most prominent visual feature upon which to infer whether individual VFSS frames belong to the pharyngeal phase. We conducted extensive experiments with multiple convolutional neural networks (CNNs) on a dataset of 1245 bolus-level clips from 59 healthy subjects. ResultsWe demonstrated that the pharyngeal phase can be detected with an F1-score higher than 0.9. Moreover, by processing the class activation maps of the CNNs, we were able to localize the bolus with promising results, obtaining correlations with ground truth trajectories higher than 0.9, without any manual annotations of bolus location used for training purposes. ConclusionsOnce validated on a larger sample of participants with swallowing disorders, our framework will pave the way for the development of intelligent tools for VFSS analysis to support clinicians in swallowing assessment.

Quantitative Distribution Characterization and Correlation Study of Composition, Structure and Hardness of Rim Region in Railway Wheel.

The railway wheel is the key component of high-speed railway train. To assure the safety in service, higher requirements are put forward in this study for the composition, microstructure uniformity, and comprehensive properties of wheel materials. In this paper, the high throughput quantitative distribution characterization methods of composition, microstructure, inclusions and Vickers hardness of high-speed railway wheel materials based on the spark source original position analysis technique, high throughput scanning electron microscope (SEM) combined with image batch processing technology, and automatic two-dimensional quantitative distribution analysis technique of inclusions and micro hardness have been studied. The distribution trend of the content of nine elements, size and quantity of sulfides and oxides, ferrite area fraction, and Vickers hardness from the wheel tread surface to the radial depth of about 50 mm below the surface has been discussed. The influence of inclusions distribution on the element segregation and the effect of rim-chilling process with different water spraying angle on the distribution of microstructure and micro hardness have been investigated. It was found that unsynchronized cooling on both sides of the rim altered the phase behavior of ferrite and pearlite and obvious inhomogeneity distribution of ferrite appeared, which led to the asymmetrical Vickers hardness in areas near or away from the flange. Based on the quantitative characterization of area fraction and micro hardness on the same location of wheel rim, a statistical mapping relationship between ferrite area fraction and Vickers hardness was established.

Application of machine learning to reflection high-energy electron diffraction images for automated structural phase mapping

We have developed a phase mapping method based on machine learning analysis of reflection high-energy electron diffraction (RHEED) images. RHEED produces diffraction patterns containing a wealth of static and dynamic information and is commonly used to determine the growth rate, the growth mode, and the surface morphology of epitaxial thin films. However, the ability to extract quantitative structural information from the RHEED patterns that appear during film growth is limited by the lack of versatile and automated analysis techniques. We have created a deep learning-based analysis method for automating the identification of different RHEED pattern types that occur during the growth of a material. Our approach combines several supervised and unsupervised machine learning techniques and permits the extraction of quantitative phase composition information. We applied this method to the mapping of the structural phase diagram of ${\mathrm{Fe}}_{\mathrm{x}}{\mathrm{O}}_{\mathrm{y}}$ thin films grown by pulsed laser deposition as a function of growth temperature and oxygen pressure close to the hematite-magnetite phase boundary. The in situ RHEED-based mapping method produces results that are qualitatively similar to postsynthesis x-ray diffraction analysis.

Object Detection Method for High Resolution Remote Sensing Imagery Based on Convolutional Neural Networks with Optimal Object Anchor Scales

ABSTRACT Object detection is a key technique for automatic information extraction, analysis and understanding of high spatial resolution remote-sensing images (HSRIs). Object anchor scales are a critical factor for object detection of HSRIs based on convolutional neural networks (CNNs). With respect to adaptively obtaining optimal object anchor scales for object detection of HSRIs, this paper proposes a novel object detection method for HSRIs based on CNNs with optimal object anchor scales. First, optimal object anchor scales for object detection of HSRIs are obtained using an adaptive object-scale learning operator. Then, a CNN object detection framework for HSRIs is designed based on optimal object anchor scales. Using multiple-object detection datasets, the proposed method is compared with some state-of-the-art object detection algorithms. Experimental results show that the proposed method can achieve 90.42%, 91.98% and 85.07% mAP on WHU-RSONE, UCAS-AOD and HSRC2016, respectively, and outperforms state-of-the-art object detection algorithms.