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BACKGROUND: The effects of traffic sign information volume (TSIV) on the visual characteristics and workload of novice and experienced drivers were investigated in this study. TSIV plays a crucial role in road traffic safety, and understanding its impact on drivers is essential for designing effective traffic sign systems. OBJECTIVE: This research aimed to compare the visual characteristics and workload of novice and experienced drivers under varying TSIV doses through simulated driving tests. The objective was to provide insights for optimizing the design of road TSIV. METHODS: Six TSIV levels were considered: S0, S1, S2, S3, S4, and S5, representing different workload levels. Participants, including both novice and experienced drivers, were involved in simulated driving scenarios with varying TSIV levels. Eye movement data was collected using an eye tracker device. The study was conducted in China, and appropriate driving simulators and equipment were utilized. RESULTS: The findings revealed several valuable results. Experienced drivers exhibited a higher proportion of saccade behavior in the 30–90 ms time period and did not show rapid saccade behavior during the 0–30 ms period, indicating superior visual search strategies. Both novice and experienced drivers demonstrated improved visual cognitive abilities at the S3 level of TSIV, which corresponds to normal and safe driving conditions. Furthermore, a majority of both groups had saccade amplitudes in the range of 0°–4°, with experienced drivers showing a slightly higher proportion. About 82% of experienced drivers had saccade behavior within the range of 0°–2°, compared to 75% of novice drivers. CONCLUSIONS: The study concludes that the S3 level of TSIV, corresponding to 30 bits/km, is optimal for both novice and experienced drivers. This level promotes better visual performance and reduces visual workload, indicating that drivers' information acquisition capacity and visual search strategies are maximized while keeping the workload associated with driving at a minimum. These findings have significant implications for enhancing driving safety. [ABSTRACT FROM AUTHOR]

More collisions occur at the exit ramps of expressways due to frequent lane-changing behavior and interweaving between vehicles. Young drivers with shorter driving mileage and driving experience, radical driving styles, and worse behavior prediction are likelier to be involved in collisions at the exit ramps. This paper focuses on the correlation analysis between young drivers' characteristics and their visual and physiological attributes at expressway exit ramps. First, the driver's gender, driving experience, and mileage are classified. Then, seven expressway exit models are established using the UC/Win road modeling software. The driver's driving plane vision is divided into four areas using the K-means clustering algorithm. In addition, the driver's visual and heart rate attributes were analyzed at 500 m, 300 m, 200 m, and 100 m away from an expressway exit. The results show that the visual attributes, gender, and driving mileage of young drivers strongly correlate with the fixation times and average saccade amplitude. In contrast, the driving experience has almost no correlation with the fixation behavior of young drivers. Young drivers' driving experience and mileage strongly correlate with cardiac physiological attributes, but there is virtually no correlation with gender. The practical implications of these results should be helpful to highway planners and designers. [ABSTRACT FROM AUTHOR]

(1) The early assessment of wood quality, even while trees are standing, provides significant benefits for forest management, sales efficiency, and market diversification. Its definition cannot be in absolute terms but must always be linked to the material's intended use. (2) In this contribution, a review of the scientific literature is given to discuss the visually evaluable attributes that define wood quality in standing trees, the applicability of the techniques used for their assessment, and the effectiveness of these attributes and technologies in predicting quality, to finally highlight future research needs. (3) The visual characteristics generally used to evaluate wood quality are linked to stem form and dimension, branchiness, and stem damage, but their assessment is challenging due to time and resource constraints. To address these challenges, laser-based and image-based techniques have been applied in field surveys. (4) Laser scanners offer detailed and accurate measurements. Photogrammetry, utilizing images to reconstruct 3D models, provides a cost-effective and user-friendly alternative. Studies have demonstrated the effectiveness of these tools in surveying the visible properties of stems and branches, but further development is necessary for widespread application, particularly in software development, with faster and more effective algorithmic advancements for automatic recognition and subsequent measurement of pertinent characteristics being critical for enhancing tool usability. (5) However, predicting wood quality from these surveys remains challenging, with a limited correlation between the visible tree characteristics assessed and the sawn product quality. Empirical studies evaluating products downstream in the forest-wood supply chain could provide valuable insights. In this sense, the implementation of traceability systems could facilitate the linkage between data on standing trees and the quality of the sawn product. Also, further research is needed to develop models that can accurately predict internal tree characteristics and their impact on product quality. [ABSTRACT FROM AUTHOR]

• Different visual environments at freeway tunnel entrance zones have varying effects on driver's visual attention, cognitive workload, and subjective mental workload. • The billboard condition attracts the highest level of visual attention from drivers, while the tip slogan condition results in the highest cognitive workload. • Evaluation using the NASA-TLX scale indicates that the tip slogan condition has the greatest impact on drivers' subjective mental workload. This study aims to evaluate the impact of different visual attraction environments at the entrance zone of freeway tunnels on drivers' visual performance and subjective mental workload. Through a naturalistic driving experiment conducted on actual roads, participants experienced various visual attraction environments at the entrance zones of freeway tunnels. The selected visual environments included baseline, landscape-style architecture, tip slogan, and billboard scenarios. Objective visual performance was assessed using eye-tracking devices to collect participants' eye movement data, while subjective mental workload was evaluated through the NASA-TLX measurement reports administered after the driving experiment. The results indicate that the visual attraction environments at the entrance zone of freeway tunnels have significant impacts on drivers' visual attention level, cognitive workload, and subjective mental workload. Under the billboard condition, participants exhibited the longest first fixation duration (FFD) and paid the earliest attention to visual attraction (reflected by participants positioned their initial fixation at the visual attraction farthest from the tunnel entrance zone (DFT)). Additionally, the tip slogan condition resulted in the highest mean fixation duration (MFD), the largest percentage of total fixation duration on the visual attraction (PTFD), and the highest cognitive workload (reflected in maximum pupil diameter (PD)). Billboard at the entrance zone of freeway tunnel had the greatest effect on attracting drivers' visual attention, while tip slogan resulted in the highest cognitive workload on drivers. The results of the subjective mental workload evaluation (NASA-TLX) also indicated that tip slogan had the greatest impact on drivers' subjective mental workload. This study provides valuable insights into the potential impact of different visual attraction environments at the entrance zone of freeway tunnels on drivers' eye-catching effect. The results contribute to both theoretical knowledge and practical applications in the field of transportation safety. [ABSTRACT FROM AUTHOR]

Safe driving depends on drivers' ability to rapidly and accurately process information in varying traffic conditions. The presence of central green landscapes on roads is a key factor in this context. However, there is a gap in current research, which tends to focus on qualitative aspects of landscape design while lacking quantitative data-driven analyses. In this study, we aim to address this gap by investigating the impact of road central greening configuration on the eye movements of young novice drivers, a population particularly sensitive to external environmental changes. Specifically, we explore the influence of central green landscapes on four visual parameters: driver gaze, saccade, blinking, and pupil behavior. Through real vehicle experiments conducted on different road sections, we collected visual feature data to comprehensively analyze the patterns of driver eye movements. Our findings reveal that the introduction of central green landscapes can exert cognitive pressure on drivers, leading to specific alterations in their visual parameters. These changes include dispersed gaze points, reduced effective gaze durations, increased gaze frequencies, extended saccade durations and angles, heightened blink durations and frequencies, and reduced pupil area. By shedding light on the intricate interplay between road central greenery and driver behavior, this study provides valuable insights for optimizing landscape design in transportation planning and enhancing road safety considerations. [ABSTRACT FROM AUTHOR]

In recent years, some expressway tunnels have started broadcasting warning sounds, such as fire alarms, to improve driver attention and traffic safety. However, there is few research on it, and in contrast to previous studies, we have considered different evaluation indices and through field measurement to determine the effectiveness of this practice. The characteristics of three warning sound signals, i.e., fire alarm, dynamic music, and voice command, in a tunnel were analyzed usingMATLAB. Considering pupil diameter and blink duration as evaluation indexes, the change in the mental state of the driver after hearing a warning sound was analyzed. Based on Markov chain theory, the change in the gaze region and gaze shift of the driver under the condition of a warning sound was analyzed. Results shows fire alarms and voice commands can increase the mental load of drivers, but the degree of impact was not determined. Dynamic music does not affect the mental load of the driver. The fire alarm and dynamic music attracted the attention of the driver; conversely, as the voice command warns the driver to focus on safety, it did not impact the attention of the driver. The research results provide a scientific reference for the selection of warning sounds in expressway tunnels and new research ideas for the prevention of traffic injuries in expressway tunnels.

• The driver's PRT was measured according to the visual fixation characteristics (using an eye-tracking device) and vehicle running data. • The PRT was measured in the interchange exit ramps. • These findings should serve as a valuable reference for refining the design of plateau expressway exit ramps and providing a theoretical basis for improving road safety in the plateau areas. The low-pressure (and low-oxygen) environment in plateau regions significantly impacts the driver's perception-reaction time (PRT) and, in turn, road safety. However, drivers faced increased workloads at interchange exit areas while the Tibet region experienced a significant improvement in high-grade highway construction. Therefore, this study aimed to quantitatively analyze the influence of the plateau environment on PRT. Using a UC/win-road simulator, 6 scenarios of expressway exit ramps were constructed. Then, a total of 50 participants (35 males, 15 females) in Nanjing (altitude of 50 m) and 50 participants (36 males, 14 females) in Lhasa (altitude of 3650 m) were recruited for the simulation experiments. The driver's PRT was measured according to the visual fixation characteristics (using an eye-tracking device) and vehicle running data. The no-difference in PRT for various scenarios was validated using the one-way-ANOVA test. Then, the Spearman bivariate correlation and generalized linear regression model were used to analyze the influence of driver's age, driving experience, gender, and acclimation period on PRT. In addition, the difference in PRT of different groups of plain and plateau drivers was further analyzed using the Mann-Whiteney U and Kruskal-Wallis tests. Although driving experience and gender affected the PRT of the plain and plateau drivers, the effect was greater in the plain areas. The low-pressure environment at high altitude had a more significant impact on young novice male drivers. These findings serve as valuable references for refining the design of expressway exit ramps and improving road safety in the plateau areas. [ABSTRACT FROM AUTHOR]

The common disorder, Keratoconus (KC), is distinguished by cumulative corneal slimming and steepening. The corneal ring implantation has become a successful surgical procedure to correct the KC patient’s vision. The determination of suitable patients for the surgery alternative is among the paramount concerns of ophthalmologists. To reduce the burden on them and enhance the treatment, this research aims to previse the ocular condition of KC patients after the corneal ring implantation. It focuses on predicting post-surgical corneal topographic indices and visual characteristics. This study applied an efficacious artificial neural network approach to foretell the aforementioned ocular features of KC subjects 6 and 12 months after implanting KeraRing and MyoRing based on the accumulated data. The datasets are composed of sufficient numbers of corneal topographic maps and visual characteristics recorded from KC patients before and after implanting the rings. The visual characteristics under study are uncorrected visual acuity (UCVA), sphere (SPH), astigmatism (Ast), astigmatism orientation (Axe), and best corrected visual acuity (BCVA). In addition, the statistical data of multiple KC subjects were registered, including three effective indices of corneal topography (i.e., Ast, K-reading, and pachymetry) pre- and post-ring embedding. The outcomes represent the contribution of practical training of the introduced models to the estimation of ocular features of KC subjects following the implantation. The corneal topographic indices and visual characteristics were estimated with mean errors of 7.29% and 8.60%, respectively. Further, the errors of 6.82% and 7.65% were respectively realized for the visual characteristics and corneal topographic indices while assessing the predictions by the leave-one-out cross-validation (LOOCV) procedure. The results confirm the great potential of neural networks to guide ophthalmologists in choosing appropriate surgical candidates and their specific intracorneal rings by predicting post-implantation ocular features.

Considerable evidence suggests that the decline in physiological abilities prevalent in older drivers leads to a reduction in the visual and psychomotor functions required for safe driving. The purpose of this study is to further investigate the differences in driving behavior between older and younger drivers and to describe the change process of driving behavior. In this study, 19 younger and older drivers each were recruited for a driving simulation experiment that included five scenarios. Driving operation data, eye movement data, and physiological data of drivers in five conflict scenarios were collected. The differences in driving behaviors between the two groups were also compared and analyzed, on which the thresholds of different driving behavior nodes were determined and driving behavior graphs were established. The results show that the eye movement nodes of older drivers appear later in five scenarios, the operational nodes of older people appear later in two steering scenarios, and are closer to those of younger drivers in three straight ahead scenarios, indicating that older drivers were later in observing and collecting traffic information, and later in applying brakes and steering to avoid conflicts when steering. The study provides a reference for the analysis of driving behavior and driving safety of older people.

This work proposes an augmented reality serious game (ARSG) for supporting individuals with motor disabilities while controlling robotic wheelchairs. A racing track was used as the game narrative; this included restriction areas, static and dynamic virtual objects, as well as obstacles and signs. To experience the game, a prior configuration of the environment, made through a smartphone or a computer, was required. Furthermore, a visualization tool was developed to exhibit user performance while using the ARSG. Two user studies were conducted with 10 and 20 participants, respectively, to compare (1) how different devices enable configuring the ARSG, and (2) different tracking capabilities, i.e., methods used to place virtual content on the real-world environment while the user interacts with the game and controls the wheelchair in the physical space: C1—motion tracking using cloud anchors; C2—offline motion tracking. Results suggest that configuring the environment with the computer is more efficient and accurate, in contrast to the smartphone, which is characterized as more engaging. In addition, condition C1 stood out as more accurate and robust, while condition C2 appeared to be easier to use. [ABSTRACT FROM AUTHOR]

Augmented Reality (AR) solutions are emerging in multiple scenarios of application as Industry 4.0 takes shape. In particular, for remote collaboration, flexible mechanisms such as authoring tools can be used to generate instructions and assist human operators as they experience increased complexity in their daily tasks. In addition to the traditional handicap of ensuring instructions can be intuitively created without having to understand complicated AR concepts, another relevant topic is the fact that the quality of said instructions is not properly analyzed prior to the tools being evaluated. This means that the characteristics of the visual content are not adequately assessed beforehand. Hence, it is essential to be aware of the cognitive workload associated with AR instructions to assert if they can be easily understood and accepted before being deployed in real-world scenarios. To address this, we focused on AR during sessions of remote guidance. Based on a participatory process with domain experts from the industry sector, a prototype for creating AR-based instructions was developed, and a user study with two parts was conducted: (1) first, a set of step-by-step instructions was produced, and their visual characteristics were evaluated by 129 participants based on a set of relevant dimensions; (2) afterward, these instructions were used by nine participants to understand if they could be used to assist on-site collaborators during real-life remote maintenance tasks. The results suggest that the AR instructions offer low visual complexity and considerable visual impact, clarity, and directed focus, thus improving situational understanding and promoting task resolution. [ABSTRACT FROM AUTHOR]

In order to improve the traffic safety at the entrance and exit of subsea tunnel, the variation law of driver􀆳s visual characteristics under different visual deceleration marking schemes at the entrance and exit of subsea tunnel was studied. Six visual deceleration marking schemes were set up, and the indoor driving simulation experiments were carried out on 26 drivers. The data such as vehicle speed, proportion of view points, pupil diameter and detection distance were collected. Through analyzing and processing the data to judge the effectiveness of visual deceleration marking, the regression models of vehicle speed and driver pupil diameter at the entrance and exit of subsea tunnel were established respectively, and the relationship between them was nonlinear at the entrance of subsea tunnel, the relationship between them was linear at the exit of subsea tunnel. The validity of visual deceleration marking was verified by driver questionnaire. The results show that the six visual deceleration marking schemes at the entrance and exit of subsea tunnel have a significant impact on the driver's visual characteristics. Compared with other marking schemes, the setting of white fishbone deceleration marking has the greatest impact on the driver's pupil diameter, the proportion of view points in area A, the detection distance and illusion influence, which helps the driver actively reduce the speed, take deceleration measures in advance and ensure driving safety. [ABSTRACT FROM AUTHOR]

The head-mounted eye-tracking technology is often used to manipulate the motion of servo platform in remote tasks, so as to achieve visual aiming of servo platform, which is a highly integrated human-computer interaction effect. However, it is difficult to achieve accurate manipulation for the uncertain meanings of gaze points in eye-tracking. To solve this problem, a method of classifying gaze points based on a conditional random field is proposed. It first describes the features of gaze points and gaze images, according to the eye visual characteristic. An LSTM model is then introduced to merge these two features. Afterwards, the merge features are learned by CRF model to obtain the classified gaze points. Finally, the meaning of gaze point is classified for target, in order to accurately manipulate the servo platform. The experimental results show that the proposed method can classify more accurate target gaze points for 100 images, the average evaluation values Precision = 86.81%, Recall = 86.79%, We = 86.79%, these are better than relevant methods. In addition, the isolated gaze points can be eliminated, and the meanings of gaze points can be classified to achieve the accuracy of servo platform visual aiming. [ABSTRACT FROM AUTHOR]

Connected vehicle technology relying on Human Machine Interface (HMI) achieve a dominant position in the overall safety improvement. However, the impact of HMI on the driver's visual attention cannot be ignored, especially on the accident-prone foggy freeway. The objective of this paper is to evaluate the level of distraction caused by HMI in data analysis of drivers' visual characteristics and to establish a generic evaluation methodology. A connected vehicle test platform has been established based on the driving simulator, in which visibility was set to the level of heavy fog and the technical condition was set in two conditions (with or without HMI). Measurement of driving behavior parameters include frequency of fixations and saccades and the proportion of fixation. The researchers compared and analyzed the driver's visual characteristics and the degree of distraction in a combination of indices based on the AttenD algorithm, setting two technical conditions in a heavy fog. Drivers suffering more visual distraction and interference with HMI may have an impact on the driver's driving safety. The results provide a generic approach to evaluate the HMI of a connected vehicle system and a safety assessment methodology for the connected vehicle system. [ABSTRACT FROM AUTHOR]

The lighting design of a road tunnel focuses on the setting of pavement luminance. As for the tunnel sidewall luminance, it simply follows the principle of no less than 60% of the pavement luminance. In fact, the sidewall is one of the important components of the tunnel lighting environment; however, the impact of the improvement of sidewall brightness on the LED lighting environment in a tunnel has not attracted enough attention. In this study, the impact of the improvement of tunnel sidewall brightness on the lighting environment and visual characteristics of human eyes were analyzed based on the lighting experiments when the tunnel sidewalls were decorated with two different types of materials and illuminated by LED lamps with five different color temperatures. The test results show that the tunnel sidewall luminance will increase if the energy-storage and luminescent coating with high reflectance is decorated on tunnel sidewalls, and the pavement luminance increases with the increasing sidewall luminance. After the improvement of the sidewall and pavement luminance, the visible distance for a small object in the tunnel will increase to a certain extent. Furthermore, there is no discomfort for drivers during driving, and the changing of eye pupil diameter is relatively stable, which indicates that the increase in tunnel sidewall luminance has a positive effect on the visual performance of drivers. [ABSTRACT FROM AUTHOR]

Although investigators are using data sources to describe the visual characteristics of streets, few researchers have linked human perceptions of the street environment with human activity density. This study proposes a conceptualized analytical framework that explains the relationship between human activity density and the visual characteristics of the streetscape. The image-segmentation model DeepLabv3+ automatically extracts each pixel's semantic information and classifies visual elements from 120,012 collected panoramic street view images of Zhengzhou, China, using the entropy weighting method and weighted superposition to calculate the street perception summary score. This deep learning approach can successfully describe the semantics of streets and the connection between population density and street perception. The study provides a new quantitative method for urban planning and the development of high-density cities. [ABSTRACT FROM AUTHOR]

Featured Application: The magnetic particle indication characteristics of bearing ring surfaces in the fluorescent magnetic particle inspection and crack detection method based on image processing were investigated, providing a valuable reference for automatic crack detection in magnetic particle inspection. Fluorescent magnetic particle inspection (MPI) is a conventional non-destructive testing process for railway bearing rings that still needs to be completed manually. Due to the complexity of bearing ring surfaces in inspection, automatic detection for bearing rings based on image processing is difficult to apply. Therefore, we proposed a bearing ring defect identification method based on visual characteristics and high-level features. Inspired by the mechanism of human visual perception, defects can be identified from the complex background conveniently by human eyes. According to the linear structure characteristics and greyscale distribution characteristics of cracks in the acquired images, we introduce the centerline extraction and Gaussian similarity measure to reduce background noise and obtain the crack candidate regions. Then, an improved MobileNetV3 is used to extract high-level features of the candidate regions and determine whether they are defective, which uses a new attention module, Coordinate Attention (CA), to substitute the Squeeze-and-Excitation (SE) attention to improve the performance. The experimental results show that the detection accuracy rate of the proposed method is 96.5%. Compared with traditional methods, the proposed method can efficiently extract crack defects in a complex textured background and shows high-quality performance in recall and precision. [ABSTRACT FROM AUTHOR]

Augmented Reality (AR) solutions are emerging in multiple scenarios of application as Industry 4.0 takes shape. In particular, for remote collaboration, flexible mechanisms such as authoring tools can be used to generate instructions and assist human operators as they experience increased complexity in their daily tasks. In addition to the traditional handicap of ensuring instructions can be intuitively created without having to understand complicated AR concepts, another relevant topic is the fact that the quality of said instructions is not properly analyzed prior to the tools being evaluated. This means that the characteristics of the visual content are not adequately assessed beforehand. Hence, it is essential to be aware of the cognitive workload associated with AR instructions to assert if they can be easily understood and accepted before being deployed in real-world scenarios. To address this, we focused on AR during sessions of remote guidance. Based on a participatory process with domain experts from the industry sector, a prototype for creating AR-based instructions was developed, and a user study with two parts was conducted: (1) first, a set of step-by-step instructions was produced, and their visual characteristics were evaluated by 129 participants based on a set of relevant dimensions; (2) afterward, these instructions were used by nine participants to understand if they could be used to assist on-site collaborators during real-life remote maintenance tasks. The results suggest that the AR instructions offer low visual complexity and considerable visual impact, clarity, and directed focus, thus improving situational understanding and promoting task resolution.

The head-mounted eye-tracking technology is often used to manipulate the motion of servo platform in remote tasks, so as to achieve visual aiming of servo platform, which is a highly integrated human-computer interaction effect. However, it is difficult to achieve accurate manipulation for the uncertain meanings of gaze points in eye-tracking. To solve this problem, a method of classifying gaze points based on a conditional random field is proposed. It first describes the features of gaze points and gaze images, according to the eye visual characteristic. An LSTM model is then introduced to merge these two features. Afterwards, the merge features are learned by CRF model to obtain the classified gaze points. Finally, the meaning of gaze point is classified for target, in order to accurately manipulate the servo platform. The experimental results show that the proposed method can classify more accurate target gaze points for 100 images, the average evaluation values Precision = 86.81%, Recall = 86.79%, We = 86.79%, these are better than relevant methods. In addition, the isolated gaze points can be eliminated, and the meanings of gaze points can be classified to achieve the accuracy of servo platform visual aiming.

The lighting design of a road tunnel focuses on the setting of pavement luminance. As for the tunnel sidewall luminance, it simply follows the principle of no less than 60% of the pavement luminance. In fact, the sidewall is one of the important components of the tunnel lighting environment; however, the impact of the improvement of sidewall brightness on the LED lighting environment in a tunnel has not attracted enough attention. In this study, the impact of the improvement of tunnel sidewall brightness on the lighting environment and visual characteristics of human eyes were analyzed based on the lighting experiments when the tunnel sidewalls were decorated with two different types of materials and illuminated by LED lamps with five different color temperatures. The test results show that the tunnel sidewall luminance will increase if the energy-storage and luminescent coating with high reflectance is decorated on tunnel sidewalls, and the pavement luminance increases with the increasing sidewall luminance. After the improvement of the sidewall and pavement luminance, the visible distance for a small object in the tunnel will increase to a certain extent. Furthermore, there is no discomfort for drivers during driving, and the changing of eye pupil diameter is relatively stable, which indicates that the increase in tunnel sidewall luminance has a positive effect on the visual performance of drivers.

Although investigators are using data sources to describe the visual characteristics of streets, few researchers have linked human perceptions of the street environment with human activity density. This study proposes a conceptualized analytical framework that explains the relationship between human activity density and the visual characteristics of the streetscape. The image-segmentation model DeepLabv3+ automatically extracts each pixel’s semantic information and classifies visual elements from 120,012 collected panoramic street view images of Zhengzhou, China, using the entropy weighting method and weighted superposition to calculate the street perception summary score. This deep learning approach can successfully describe the semantics of streets and the connection between population density and street perception. The study provides a new quantitative method for urban planning and the development of high-density cities.

Fluorescent magnetic particle inspection (MPI) is a conventional non-destructive testing process for railway bearing rings that still needs to be completed manually. Due to the complexity of bearing ring surfaces in inspection, automatic detection for bearing rings based on image processing is difficult to apply. Therefore, we proposed a bearing ring defect identification method based on visual characteristics and high-level features. Inspired by the mechanism of human visual perception, defects can be identified from the complex background conveniently by human eyes. According to the linear structure characteristics and greyscale distribution characteristics of cracks in the acquired images, we introduce the centerline extraction and Gaussian similarity measure to reduce background noise and obtain the crack candidate regions. Then, an improved MobileNetV3 is used to extract high-level features of the candidate regions and determine whether they are defective, which uses a new attention module, Coordinate Attention (CA), to substitute the Squeeze-and-Excitation (SE) attention to improve the performance. The experimental results show that the detection accuracy rate of the proposed method is 96.5%. Compared with traditional methods, the proposed method can efficiently extract crack defects in a complex textured background and shows high-quality performance in recall and precision.

Digital x-ray medical image with human visual characteristics is a kind of medical image formed by x-ray irradiation. Currently, medical image processing algorithm is the main hot topic. With the popularization of medical images in clinical diagnosis and the development of computer technology, the accuracy of clinical diagnosis can be greatly improved using medical image processing technology. For digital x-ray imaging in medical system, it is inevitable that the quality of medical image will be degraded by various factors, so it needs to be processed before they can be used. At the same time, with the development of medical digital x-ray detector technology, the range of medical images used will be much smaller than that of detectors. The medical digital x-ray image data received after detection also need to be processed by an effective image-processing algorithm before display. On the other hand, the research, analysis, and diagnostic use of computer in medical images can appropriately reduce the work intensity of doctors or medical staff, and also play an important role in the discovery of certain diseases. A method of digital x-ray medical image enhancement based on human visual characteristics is studied by analyzing the characteristics of histogram of x-ray image and the maximum entropy fuzzy threshold image enhancement algorithm. The experimental results show that the method can fully recognize the medical x-ray images with incorrect exposure, enhancing the effect of digital x-ray imaging and improving the recognition rate. [ABSTRACT FROM AUTHOR]

• Nine typical traffic hazard scenarios were carried out at Linzhi, Lhasa, Naqu, and Yanghu Scenic Area based on UC-WIN / ROAD driving simulation software. • Drivers' visual search modes in the plateau areas were analyzed according to drivers' eye movement data collected by ASL Mobile Eye monocular eye tracker. • The increase of altitude would weaken the drivers' visual sensitivity and cognitive processing ability of hazard information, which would reduce drivers' hazard perception skills, and the increase of driving experience might help alleviate such negative impacts to some extent. A good visual search mode is an important prerequisite for a driver to perceive the hazard in the traffic environment timely and accurately. However, hypoxia conditions in the plateau environment might affect drivers' cognitive and judgment ability, posing potential threats to safe driving. This study analyzed the eye movement behaviors of drivers when faced with traffic hazards in the plateau environment, to examine the impact level of altitude on drivers' perception and reaction. Nine typical traffic hazard scenarios were investigated at four locations with four different altitudes, including Linzhi, Lhasa, Naqu, and Yanghu Scenic Area based on UC-WIN / ROAD driving simulation software. Then, drivers' visual search modes were analyzed according to drivers' eye movement data collected by ASL Mobile Eye monocular eye tracker. As the altitude increased, the drivers' first fixation time and the average saccade amplitude decreased, while the fixation duration percentage increased. Drivers with fewer years of driving experience had a larger percentage of fixation duration and a smaller saccade amplitude. In addition, a shorter acclimation period also negatively influenced the percentage of fixation time. The increase of altitude would weaken the drivers' visual sensitivity and cognitive processing ability of hazard information, which would reduce drivers' hazard perception skills, and the increase of driving experience might help alleviate such negative impacts to some extent. Based on the visual characteristics of traffic hazard scenarios obtained in this paper, the training of highly accident-prone drivers can be guided specifically to improve their visual search strategies, thereby improving driving safety. [ABSTRACT FROM AUTHOR]