Mark Detection Research Articles

YOLO-Highway: An Improved Highway Center Marking Detection Model for Unmanned Aerial Vehicle Autonomous Flight

Automatic visual navigation flight of an unmanned aerial vehicle (UAV) plays an important role in the highway maintenance field. Automatic highway center marking detection is the most important part of the visual navigation flight of a UAV. In this study, the UAV-viewed highway data are collected from the UAV perspective. This paper proposes a model named the YOLO-Highway that uses an improved form of the You Only Look Once (YOLO) model to enhance the real-time detection of highway marking problems. The proposed model is mainly designed by optimizing the network structure and the loss function of the original YOLOv3 model. The proposed model is verified by the experiments using the highway center marking dataset, and the results show that the average precision (AP) of the trained model is 82.79%, and the frames per second (FPS) is 25.71 f/s. In comparison with the original YOLOv3 model, the detection accuracy of the proposed model is improved by 7.05%, and its speed is improved by 5.29 f/s. Moreover, the proposed model had stronger environmental adaptability and better detection precision and speed than the original model in complex highway scenarios. The experimental results show that the proposed YOLO-Highway model can accurately detect the highway center markings in real-time and has high robustness to changes in different environmental conditions. Therefore, the YOLO-Highway model can meet the real-time requirements of the highway center marking detection.

Transfer Learning for LiDAR-Based Lane Marking Detection and Intensity Profile Generation

Recently, light detection and ranging (LiDAR)-based mobile mapping systems (MMS) have been utilized for extracting lane markings using deep learning frameworks. However, huge datasets are required for training neural networks. Furthermore, with accurate lane markings being detected utilizing LiDAR data, an algorithm for automatically reporting their intensity information is beneficial for identifying worn-out or missing lane markings. In this paper, a transfer learning approach based on fine-tuning of a pretrained U-net model for lane marking extraction and a strategy for generating intensity profiles using the extracted results are presented. Starting from a pretrained model, a new model can be trained better and faster to make predictions on a target domain dataset with only a few training examples. An original U-net model trained on two-lane highways (source domain dataset) was fine-tuned to make accurate predictions on datasets with one-lane highway patterns (target domain dataset). Specifically, encoder- and decoder-trained U-net models are presented wherein, during retraining of the former, only weights in the encoder path of U-net were allowed to change with decoder weights frozen and vice versa for the latter. On the test data (target domain), the encoder-trained model (F1-score: 86.9%) outperformed the decoder-trained (F1-score: 82.1%). Additionally, on an independent dataset, the encoder-trained one (F1-score: 90.1%) performed better than the decoder-trained one (F1-score: 83.2%). Lastly, on the basis of lane marking results obtained from the encoder-trained U-net, intensity profiles were generated. Such profiles can be used to identify lane marking gaps and investigate their cause through RGB imagery visualization.

Proof of Concept for the Detection of Local Pressure Marks in Prosthesis Sockets Using Structural Dynamics Measurement

The wear comfort of a prosthesis is of great importance for amputee patients. The wear comfort can be affected by changes in the interface between the residual limb and prosthesis socket, which can be caused by time-dependent volume fluctuations of the tissue, leading to unwanted local pressure marks. The basis to ensure time-independent wear comfort of a prosthesis is to identify these changes. Common techniques for identifying these variations have a negative impact on the sensitive interface between the residual limb and prosthesis. The following paper contains a proof of concept for the detection of local pressure marks without affecting the described interface using structural dynamics measurements, exemplarily shown at a prosthetic socket for transfemoral amputees in a test bench scenario. The dynamical behaviour of the investigated system is analysed in the form of frequency response functions acquired for different pressure locations and preloads using an impact hammer for excitation and a triaxial acceleration sensor. The frequency response functions show major changes for the various boundary conditions with respect to their frequency-dependent compositions. The results demonstrate how the utilised method enables the identification of changes in local pressure marks regarding the variation of position and magnitude.

Pseudopneumothorax: diagnostic pitfalls

Evidence of pneumothorax on radiograph of the chest without accompanying major respiratory signs is rare. Various artefacts including skin fold, shirt fold, bed sheet fold and medial border of scapula...

Detecting the epitranscriptome.

RNA modifications and their corresponding epitranscriptomic writer and eraser enzymes regulate gene expression. Altered RNA modification levels, dysregulated writers, and sequence changes that disrupt epitranscriptomic marks have been linked to mitochondrial and neurological diseases, cancer, and multifactorial disorders. The detection of epitranscriptomics marks is challenging, but different next generation sequencing (NGS)-based and mass spectrometry-based approaches have been used to identify and quantitate the levels of individual and groups of RNA modifications. NGS and mass spectrometry-based approaches have been coupled with chemical, antibody or enzymatic methodologies to identify modifications in most RNA species, mapped sequence contexts and demonstrated the dynamics of specific RNA modifications, as well as the collective epitranscriptome. While epitranscriptomic analysis is currently limited to basic research applications, specific approaches for the detection of individual RNA modifications and the epitranscriptome have potential biomarker applications in detecting human conditions and diseases. This article is categorized under: RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems RNA Processing > tRNA Processing RNA in Disease and Development > RNA in Disease.

Do We Need a Change in Road Winter Maintenance to Accommodate for Automated Vehicles? A State-of-the-Art Literature Review Considering Automated Vehicle Technology’s Usage of Road Infrastructure During Winter

For automated vehicles to be allowed to join the modern car fleet, and, in the future, replace human drivers, they must be able to handle adverse weather, including snowy conditions. This literature review focuses on how automated vehicles utilize the road and how this use is suitable for winter maintenance strategies. Where global navigation satellite system (GNSS) service is unavailable, automated vehicles need bare roads to perform relative navigation based on real-time data about lane markings, obstacles, and road infrastructure. Snow-covered tracks hinder vehicle navigation and lane marking detection, which might generate wheel slippage that in turn causes emergency stop and challenging friction estimates. Although the entry of automated vehicles into the car fleet does not demand change in the strategies of winter maintenance, it does demand higher level of service than today. Maintaining an entire road network on which autonomous vehicles always can operate is tremendously expensive and likely not feasible. One solution could be to add another maintenance class in a bare road strategy, that is, an automated vehicle maintenance class with a high level of service and a set of operational criteria allowing automated vehicles to operate. The maintenance class should be used for certain main routes where there is a high frequency of automated vehicles. A model that recommends preferable routes to the destination based on current road conditions within the operational envelope should be provided to the automated vehicle system.

Liquid Level Detection in Standard Capacity Measures with Machine Vision.

Capacity measures are commonly used volume standards for testing measuring systems for liquids other than water. Manual readings from the measuring scale can often be difficult due to the location of the capacity measure or to the nature of the measured liquid. This article focuses on the automation of this procedure by using a single camera machine vision system. A camera positioned perpendicular to the transparent neck captures the image of the liquid meniscus and the measuring scale. The volume reading is determined with the user-defined software in the LabVIEW programming environment, which carries out the image preprocessing, detection of the scale marks and the liquid level, correction of lens distortion and parallax effects and final unit conversions. The realized measuring system for liquid level detection in standard capacity measures is tested and validated by comparing the automated measurement results with those taken by the operators. The results confirm the appropriateness of the presented measuring system for the field of legal metrology.

Deep Learning in Lane Marking Detection: A Survey

Lane marking detection is a fundamental but crucial step in intelligent driving systems. It can not only provide relevant road condition information to prevent lane departure but also assist vehicle positioning and forehead car detection. However, lane marking detection faces many challenges, including extreme lighting, missing lane markings, and obstacle obstructions. Recently, deep learning-based algorithms draw much attention in intelligent driving society because of their excellent performance. In this paper, we review deep learning methods for lane marking detection, focusing on their network structures and optimization objectives, the two key determinants of their success. Besides, we summarize existing lane-related datasets, evaluation criteria, and common data processing techniques. We also compare the detection performance and running time of various methods, and conclude with some current challenges and future trends for deep learning-based lane marking detection algorithm.

Vision-Based Pavement Marking Detection and Condition Assessment—A Case Study

Pavement markings constitute an effective way of conveying regulations and guidance to drivers. They constitute the most fundamental way to communicate with road users, thus, greatly contributing to ensuring safety and order on roads. However, due to the increasingly extensive traffic demand, pavement markings are subject to a series of deterioration issues (e.g., wear and tear). Markings in poor condition typically manifest as being blurred or even missing in certain places. The need for proper maintenance strategies on roadway markings, such as repainting, can only be determined based on a comprehensive understanding of their as-is worn condition. Given the fact that an efficient, automated and accurate approach to collect such condition information is lacking in practice, this study proposes a vision-based framework for pavement marking detection and condition assessment. A hybrid feature detector and a threshold-based method were used for line marking identification and classification. For each identified line marking, its worn/blurred severity level was then quantified in terms of worn percentage at a pixel level. The damage estimation results were compared to manual measurements for evaluation, indicating that the proposed method is capable of providing indicative knowledge about the as-is condition of pavement markings. This paper demonstrates the promising potential of computer vision in the infrastructure sector, in terms of implementing a wider range of managerial operations for roadway management.

Computer vision with deep learning for ship draft reading

The draft is a measurement of the vertical distance between the waterline and the bottom of the ship hull. The displacement tonnage of a ship then can be calculated by the observed draft. The current draft survey is done by surveyors, which is subject to human errors. We propose to use computer vision with deep learning for draft reading from images. First, mask R-CNN is used to segment the region of interest—draft marks and water—from images. Then UNet is used to refine the waterline detection. The detection of marks is based on the computer vision methods and the content of marks is recognized by ResNet. Finally, we can infer the draft of a ship based on the extracted visual information. Experimental results on a realistic dataset have shown that the proposed method can perform the task of draft reading on a par with humans.

A Road Marking Detection System Using Partial Template Matching and Region Estimation by Deep Neural Network

A Road Marking Detection System Using Partial Template Matching and Region Estimation by Deep Neural Network

The hidden waves in the ECG uncovered revealing a sound automated interpretation method

A novel approach for analysing cardiac rhythm data is presented in this paper. Heartbeats are decomposed into the five fundamental P, Q, R, S and T waves plus an error term to account for artifacts in the data which provides a meaningful, physical interpretation of the heart’s electric system. The morphology of each wave is concisely described using four parameters that allow all the different patterns in heartbeats to be characterized and thus differentiated This multi-purpose approach solves such questions as the extraction of interpretable features, the detection of the fiducial marks of the fundamental waves, or the generation of synthetic data and the denoising of signals. Yet the greatest benefit from this new discovery will be the automatic diagnosis of heart anomalies as well as other clinical uses with great advantages compared to the rigid, vulnerable and black box machine learning procedures, widely used in medical devices. The paper shows the enormous potential of the method in practice; specifically, the capability to discriminate subjects, characterize morphologies and detect the fiducial marks (reference points) are validated numerically using simulated and real data, thus proving that it outperforms its competitors.

An Automatic Lane Marking Detection Method With Low-Density Roadside LiDAR Data

Lane information is an essential part of high-resolution micro-traffic data (HRMTD). Most of the lane detection algorithms for Light Detection and Ranging (LiDAR) are applied to high-density onboard LiDAR and airborne LiDAR, which cannot be used directly to process low-density roadside LiDAR data. In this article, an algorithm for lane detection applied to low-density roadside LiDAR is proposed, which includes three steps: ground recognition, lane marking point extraction, and pavement lane marking points clustering. Firstly, an improved algorithm for ground recognition is proposed to get normal ground points (NGPs). Then, the lane marking points are extracted from the no lane marking points based on the difference of laser intensity. Finally, we divided the ground within the range of LiDAR scanning into different stripes, and search the maximum linear density of the stripe to extract the lane markings. The average distance error (ADE) of lane detection results is less than 0.1m by fusing and comparing the test results with the aerial photographs. The proposed algorithm is also proved to be robust in multiple test locations and the results can be used to assist high-precision vehicle positioning in vehicle-to-infrastructure (V2I) cooperation application within the intelligent transportation system.

Extending automatic transcripts in a unified data representation towards a prosodic-based metadata annotation and evaluation

This paper describes a framework that extends automatic speech transcripts in order to accommodate relevant information coming from manual transcripts, the speech signal itself, and other resources, like lexica. The proposed framework automatically collects, relates, computes, and stores all relevant information together in a self-contained data source, making it possible to easily provide a wide range of interconnected information suitable for speech analysis, training, and evaluating a number of automatic speech processing tasks. The main goal of this framework is to integrate different linguistic and paralinguistic layers of knowledge for a more complete view of their representation and interactions in several domains and languages. The processing chain is composed of two main stages, where the first consists of integrating the relevant manual annotations in the speech recognition data, and the second consists of further enriching the previous output in order to accommodate prosodic information. The described framework has been used for the identification and analysis of structural metadata in automatic speech transcripts. Initially put to use for automatic detection of punctuation marks and for capitalization recovery from speech data, it has also been recently used for studying the characterization of disfluencies in speech. It was already applied to several domains of Portuguese corpora, and also to English and Spanish Broadcast News corpora.

Single cell epigenetic visualization assay.

Characterization of the epigenetic status of individual cells remains a challenge. Current sequencing approaches have limited coverage, and it is difficult to assign an epigenetic status to the transcription state of individual gene alleles in the same cell. To address these limitations, a targeted microscopy-based epigenetic visualization assay (EVA) was developed for detection and quantification of epigenetic marks at genes of interest in single cells. The assay is based on an in situ biochemical reaction between an antibody-conjugated alkaline phosphatase bound to the epigenetic mark of interest, and a 5′-phosphorylated fluorophore-labeled DNA oligo tethered to a target gene by gene-specific oligonucleotides. When the epigenetic mark is present at the gene, phosphate group removal by the phosphatase protects the oligo from λ-exonuclease activity providing a quantitative fluorescent readout. We applied EVA to measure 5-methylcytosine (5mC) and H3K9Ac levels at different genes and the HIV-1 provirus in human cell lines. To link epigenetic marks to gene transcription, EVA was combined with RNA-FISH. Higher 5mC levels at the silenced compared to transcribed XIST gene alleles in female somatic cells validated this approach and demonstrated that EVA can be used to relate epigenetic marks to the transcription status of individual gene alleles.

A Multilane Tracking Algorithm Using IPDA with Intensity Feature.

Detection of multiple lane markings on road surfaces is an important aspect of autonomous vehicles. Although a number of approaches have been proposed to detect lanes, detecting multiple lane markings, particularly across a large number of frames and under varying lighting conditions, in a consistent manner is still a challenging problem. In this paper, we propose a novel approach for detecting multiple lanes across a large number of frames and under various lighting conditions. Instead of resorting to the conventional approach of processing each frame to detect lanes, we treat the overall problem as a multitarget tracking problem across space and time using the integrated probabilistic data association filter (IPDAF) as our basis filter. We use the intensity of the pixels as an augmented feature to correctly group multiple lane markings using the Hough transform. By representing these extracted lane markings as splines, we then identify a set of control points, which becomes a set of targets to be tracked over a period of time, and thus across a large number of frames. We evaluate our approach on two different fronts, covering both model- and machine-learning-based approaches, using two different datasets, namely the Caltech and TuSimple lane detection datasets, respectively. When tested against model-based approach, the proposed approach can offer as much as , , and improvements on the true positive, false positive, and false positives per frame rates compared to the best alternative approach, respectively. When compared against a state-of-the-art machine learning technique, particularly against a supervised learning method, the proposed approach offers , , , and improvements on the false positive, false negative, accuracy, and frame rates. Furthemore, the proposed approach retains the explainability, or in other words, the cause of actions of the proposed approach can easily be understood or explained.

Real-Time Road Curb and Lane Detection for Autonomous Driving Using LiDAR Point Clouds

The commercialization of automated driving vehicles promotes the development of safer and more efficient autonomous driving technologies including lane marking detection strategy, which is considered to be the most promising feature in environmental perception technology. To reduce the tradeoff between time consumption and detection precision, we propose a real-time lane marking detection method by using LiDAR point clouds directly. A constrained RANSAC algorithm is applied to select the regions of interest and filter the background data. Further, a road curb detection method based on the segment point density is also proposed to classify the road points and curb points. Finally, an adaptive threshold selection method is proposed to identify lane markings. In this investigation, five datasets are collected from different driving conditions that include the straight road, curved road, and uphill, to test the proposed method. The proposed method is evaluated under different performance metrics such as Precision, Recall, Dice, Jaccard as well as the average detection distance and computation time for the five datasets. The quantitative results show the efficiency and feasibility of this proposed method.

Multitask Attention Network for Lane Detection and Fitting.

Many CNN-based segmentation methods have been applied in lane marking detection recently and gain excellent success for a strong ability in modeling semantic information. Although the accuracy of lane line prediction is getting better and better, lane markings' localization ability is relatively weak, especially when the lane marking point is remote. Traditional lane detection methods usually utilize highly specialized handcrafted features and carefully designed postprocessing to detect the lanes. However, these methods are based on strong assumptions and, thus, are prone to scalability. In this work, we propose a novel multitask method that: 1) integrates the ability to model semantic information of CNN and the strong localization ability provided by handcrafted features and 2) predicts the position of vanishing line. A novel lane fitting method based on vanishing line prediction is also proposed for sharp curves and nonflat road in this article. By integrating segmentation, specialized handcrafted features, and fitting, the accuracy of location and the convergence speed of networks are improved. Extensive experimental results on four-lane marking detection data sets show that our method achieves state-of-the-art performance.

Facial marks for enhancing facial recognition after plastic surgery

With technological innovations soaring high researchers are able to develop more efficient and adaptable biometric recognition systems. Biometric systems work by matching different biometric traits to templates in a database. Recognition systems use soft as well as hard biometrics. Soft biometrics are facial or birth marks, gender, race etc. whereas hard biometrics include fingerprint, iris, retina, face etc. In this paper we have proposed a methodology by clubbing an existing facial recognition system with the statistics attained from facial marks in order to mend the recognition rate between before after surgically altered images of human beings. Firstly an algorithm is introduced for spontaneously identifying facial marks, which are represented using Histograms of Oriented Gradients (HOG), secondly they are accorded to their location in the face image. The proposed methodology is implemented on the plastic surgical facial dataset. Widespread experiments are lead in order to show the efficacy of the anticipated facial mark detection process, and to substantiate the benefits of using the statistics of facial marks on top of customary face acknowledgement schemes. The above mentioned methodology is till date not implemented on surgically altered facial image samples.

Development of an Automated Visibility Analysis Framework for Pavement Markings Based on the Deep Learning Approach

Pavement markings play a critical role in reducing crashes and improving safety on public roads. As road pavements age, maintenance work for safety purposes becomes critical. However, inspecting all pavement markings at the right time is very challenging due to the lack of available human resources. This study was conducted to develop an automated condition analysis framework for pavement markings using machine learning technology. The proposed framework consists of three modules: a data processing module, a pavement marking detection module, and a visibility analysis module. The framework was validated through a case study of pavement markings training data sets in the U.S. It was found that the detection model of the framework was very precise, which means most of the identified pavement markings were correctly classified. In addition, in the proposed framework, visibility was confirmed as an important factor of driver safety and maintenance, and visibility standards for pavement markings were defined.