Recognition Distance Research Articles

Does using a bioptic telescope improve visual recognition of the on-road environment?

Individuals with reduced visual acuity (VA) may drive with bioptic telescopes in some jurisdictions. The effect of bioptic telescope use on on-road recognition distances is important for driving safety, as increased recognition distances increase the time available to react to road signs or driving events. This study aimed to investigate the impact of bioptic telescope use on visual recognition of road signs, traffic lights, and hazards in an on-road driving environment in individuals with reduced VA. Ten individuals (mean ± standard deviation age, 39.1 ± 19.1 years) with reduced VA (trained to use bioptic telescopes) participated in a repeated-measures on-road experiment for two viewing conditions, with and without a bioptic telescope (randomized order). Participants underwent a clinical low vision assessment, including high-contrast VA with and without the bioptic telescope aligned, visual fields, and contrast sensitivity testing. For the driving component, participants seated in the front passenger seat of a moving car reported all road signs, traffic lights, and hazards (unpredictable road events that represent a risk to driving safety including other vehicles, cyclists, pedestrians) seen along a route that included suburban roads and highways. Video cameras captured participants' viewing behavior, verbal commentary, and the distance at which three pre-selected road signs were reported. In the eye with the bioptic telescope, high-contrast VA improved from 0.75 ± 0.17 (without) to 0.25 ± 0.1 logMAR with the bioptic telescope. Bioptic telescope use did not affect the percentage of road signs, traffic lights, or hazards correctly recognized but did result in 2.6 times longer recognition distances (49 ± 23 vs. 19 ± 11 m, t9 = 5.02, p<0.001). Road signs were recognized at significantly longer distances when using a bioptic telescope, confirming their positive impact on timely visual recognition of objects within the driving scene. Future work should explore whether this effect generalizes to individuals using bioptic telescopes when driving a vehicle.

An Adaptive Simplification Method for Coastlines Using a Skeleton Line “Bridge” Double Direction Buffering Algorithm

Aiming at the problem that the current double direction buffering algorithm is easy to use to seal the “bottleneck” area when simplifying coastlines, an adaptive simplification method for coastlines using a skeleton line “bridge” double direction buffering algorithm is proposed. Firstly, from the perspective of visual constraints, the relationship between the buffer distance and the coastline line width and the minimum recognition distance of the human eye is theoretically derived and determined. Then, based on the construction of the coastline skeleton binary tree, the “bridge” skeleton line is extracted using the “source tracing” algorithm. Finally, the shoreline adaptive simplification is realized by constructing a visual buffer of “bridge” skeleton lines to bridge the original resulting coastline and the local details. The experimental results show that the proposed method can effectively solve the problem that the current double direction buffering algorithm has, which can significantly improve the quality of simplification.

Fuzzy SVM With Mahalanobis Distance for Situational Awareness-Based Recognition of Public Health Emergencies

In public health emergencies, situational awareness is crucial for swift responses by governments and rescue organizations. In this manuscript, a novel framework is proposed to identify and classify event-specific information, aiming to comprehend concepts, characteristics, and classifications associated with situational awareness in social media emergencies. First, a statistical approach is employed to extract a set of standard features. Second, a category-based latent dirichlet allocation to vector (LDA2vec) model is leveraged to extract topic-based features to enhance accuracy, particularly for unbalanced datasets. Finally, a fuzzy support vector machine (FSVM) classifier utilizing the Mahalanobis distance kernel is introduced to improve the detection accuracy of event-specific information. The framework's effectiveness is evaluated using the social media public health dataset, achieving superior filtering capabilities for non-informative data with a precision of 89% and an F1-Score of 91%, surpassing other standard methods.

Ultra-Range Gesture Recognition using a web-camera in Human–Robot Interaction

Hand gestures play a significant role in human interactions where non-verbal intentions, thoughts and commands are conveyed. In Human–Robot Interaction (HRI), hand gestures offer a similar and efficient medium for conveying clear and rapid directives to a robotic agent. However, state-of-the-art vision-based methods for gesture recognition have been shown to be effective only up to a user-camera distance of seven meters. Such a short distance range limits practical HRI with, for example, service robots, search and rescue robots and drones. In this work, we address the Ultra-Range Gesture Recognition (URGR) problem by aiming for a recognition distance of up to 25 m and in the context of HRI. We propose the URGR framework, a novel deep-learning, using solely a simple RGB camera. Gesture inference is based on a single image. First, a novel super-resolution model termed High-Quality Network (HQ-Net) uses a set of self-attention and convolutional layers to enhance the low-resolution image of the user. Then, we propose a novel URGR classifier termed Graph Vision Transformer (GViT ) which takes the enhanced image as input. GViT combines the benefits of a Graph Convolutional Network (GCN) and a modified Vision Transformer (ViT). Evaluation of the proposed framework over diverse test data yields a high recognition rate of 98.1%. The framework has also exhibited superior performance compared to human recognition in ultra-range distances. With the framework, we analyze and demonstrate the performance of an autonomous quadruped robot directed by human gestures in complex ultra-range indoor and outdoor environments, acquiring 96% recognition rate on average.

Service performance evaluation of light-transmitting concrete lane markings on highways based on accelerated pavement testing

Applying light-transmitting concrete (LTC) to highway lane markings is a novel idea to increase the visual recognition distance and promote traffic safety. However, compared to its excellent visual recognition performance, its service performance has received little attention in the current research. This paper conducted an accelerated pavement test to investigate the behaviour of rutting depth, British Pendulum Number (BPN) and water permeability coefficient of the LTC lane markings. The results show that rutting depth of LTC is lower than that of asphalt pavement. As a result, small height steps appeared at the seam of the two parts. For the anti-sliding performance represented by the BPN, the BPN of LTC gradually exceeds asphalt pavement and maintains a slightly higher value. For the water permeability coefficient, the LTC is almost un-permeable, avoiding water damage during service. According to these findings, the LTC lane markings meet the requirements of highway service performance. .

Optimization of As(Ш) imprinted polymers and dynamic recognition mechanism revelation from perspective of target-monomer interaction

Molecular imprinted polymers (MIPs) have been recognized as good adsorbents to detect and remove As(Ш) from environment. 2-mercaptonicoic acid (2-M), having high affinity towards H3AsO3, is considered as potential functional monomer of H3AsO3 MIPs. In this work, a template-monomer binding affinity guide strategy was applied to investigate self-assembling conditions of H3AsO3 MIPs with 2-M as functional monomer, using quantum chemistry calculation and molecular dynamics simulation. Afterwards, dynamic recognition mechanism of H3AsO3 MIPs was unraveled through imprinted cavity modelling for the first time. Results show that dichloromethane is the most suitable solvent, and 1:4:250 of H3AsO3, 2-M and dichloromethane is the best ratio. Remarkably, the direct observation of dynamic evolution of targets recognition was achieved by constructed annular imprinted cavity models with optimal 20 TEOS as cross-linkers. It was found that the critical distance for H3AsO3 recognition is 3.0 Å. The dominant binding interaction is clarified to be hydrogen bond, and the initial driving force was S-H···O bond formation. This work provides not only a feasible strategy for computational design of good-performance MIPs, but also a method to perform recognition mechanism study by imprinted cavity modelling, which is significant for MIPs development as adsorbents for contaminants detection and removal from environment.

Verification of the Applicability of Obstacle Recognition Distance as a Measure of Effectiveness of Road Lighting on Rainy and Foggy Roads

Adverse weather conditions at night are very fatal to drivers, causing serious traffic accidents. Road lighting is a facility that can alleviate these dangerous situations. Nevertheless, road lighting has only rarely been studied during adverse weather. The reason is that the current road lighting performance evaluation method is presented based on normal weather. The current road lighting performance evaluation method uses a luminance meter to measure the road surface, which is not suitable due to scattering during adverse weather such as rain and fog. Therefore, this study proposes obstacle recognition distance as a measure of effectiveness to evaluate the performance of road lighting during adverse weather. There is a lack of actual research on whether obstacle recognition distance can be used as a measure of effectiveness for road lighting during adverse weather. Therefore, in this study, 30 subjects were used to measure the subjects’ obstacle recognition distance according to changes in weather conditions, road lighting grade, and road lighting color temperature. As a result, it was analyzed that there was a clear trend of change in obstacle recognition distance depending on the change in each condition. It was found that, under the same road lighting performance conditions, there was a difference of up to 72.86% by weather condition; under the same weather conditions, there was a difference of up to 22.75% by road lighting grade; and by color temperature, there was a difference of up to 21.87%. In addition, a statistical significance test was performed to support the existence of a difference, and the results were synthesized to suggest that obstacle recognition distance can be used as a performance measure of effectiveness of road lighting in adverse weather.

Triangle codes and tracer lights based absolute positioning method for terminal visual docking of autonomous underwater vehicles

PurposeDocking technology plays a crucial role in enabling long-duration operations of autonomous underwater vehicles (AUVs). Visual positioning solutions alone are susceptible to abnormal drift values due to the challenging underwater optical imaging environment. When an AUV approaches the docking station, the absolute positioning method fails if the AUV captures an insufficient number of tracers. This study aims to to provide a more stable absolute position visual positioning method for underwater terminal visual docking.Design/methodology/approachThis paper presents a six-degree-of-freedom positioning method for AUV terminal visual docking, which uses lights and triangle codes. The authors use an extended Kalman filter to fuse the visual calculation results with inertial measurement unit data. Moreover, this paper proposes a triangle code recognition and positioning algorithm.FindingsThe authors conducted a simulation experiment to compare the underwater positioning performance of triangle codes, AprilTag and Aruco. The results demonstrate that the implemented triangular code reduces the running time by over 70% compared to the other two codes, and also exhibits a longer recognition distance in turbid environments. Subsequent experiments were carried out in Qingjiang Lake, Hubei Province, China, which further confirmed the effectiveness of the proposed positioning algorithm.Originality/valueThis fusion approach effectively mitigates abnormal drift errors stemming from visual positioning and cumulative errors resulting from inertial navigation. The authors also propose a triangle code recognition and positioning algorithm as a supplementary approach to overcome the limitations of tracer light positioning beacons.

Influence of optical polarization on underwater range-gated imaging for target recognition distance under different water quality conditions

Influence of optical polarization on underwater range-gated imaging for target recognition distance under different water quality conditions

The Bit Query for Labels in a Binary Tree-Based Anti-Collision Recognition Algorithm

With its benefits including recognition distance, penetration ability, and multi-object recognition, radio frequency identification technology is a non-contact automatic identification method that is currently being used extensively in a variety of industries, including document anti-counterfeiting, automation, transportation, control management, and product services. An efficient anti-collision mechanism, such as anti-collision algorithms or anti-collision protocols, must be established in order to coordinate communication between the tag and the reader in the event that an RFID system experiences signal interference in the wireless channel, conflict, or collision, which will result in tag recognition or data collection failure. In response to tag conflicts in radio frequency identification (RFID) systems, a novel hybrid blocking query tree algorithm is suggested that leverages blocking prediction and child node hedging techniques to overcome the issues of lengthy time intervals and high communication complexity. The technique uses quadruple and bifurcated query trees as the foundation for its conflict avoidance strategy. The approach minimizes the conflict time and prevents the construction of idle subunits by forcing the reader to create new query prefixes via prediction, which is accomplished by employing locking commands to extract and forecast information about the conflicting bits. According to simulation data, this strategy performs better in terms of lowering the overall number of time slots and communication complexity as well as enhancing identification and labelling abilities than the current adaptive multi-tree search (RLAMS) and improved hybrid query tree (IHQT) techniques. It greatly increases the effectiveness of label identification while lowering the overall amount of time slots and communication complexity.

Impact of presbyopia treatment pilocarpine hydrochloride 1.25% on night-driving performance

ABSTRACT Clinical relevance Patients prescribed pilocarpine ophthalmic solution are advised to be cautious when driving at night, but studies evaluating the effects of pilocarpine hydrochloride ophthalmic solution 1.25% (pilo), approved to treat presbyopia, on driving at night are lacking. Background This double-masked, crossover, phase 3b study evaluated night-driving performance with pilo or the placebo once daily. Methods Forty-three adults (40–55 years) with presbyopia impacting daily activities and mesopic, high-contrast, binocular distance-corrected near vision 6/12–6/30 were randomised to bilateral treatment with pilo followed by placebo or placebo followed by pilo (with a ≥7-day washout between interventions). Night-driving performance was evaluated at twilight at a closed-circuit course. Primary efficacy endpoint: overall composite night-driving performance Z score at the end of the 7–14-day intervention period, 1 hour post-instillation. Pilo was considered non-inferior if the lower limit of the 95% confidence interval (CI) for the least squares mean difference (LSMD, pilo minus placebo) was >–0.25. Other efficacy endpoints: individual components of the night-driving performance test (hazard avoidance rate; road sign recognition rate and distance; pedestrians recognition distance; overall driving and lane-keeping times) and night-driving experience questionnaire. Safety included treatment-emergent adverse events (TEAEs). Results The mean overall composite Z scores were −0.121 (pilo) and 0.118 (placebo). The LSMD (pilo minus placebo) was −0.224 (95% CI, −0.346, −0.103), with 3 of the 7 individual tasks being significantly better with the placebo. The questionnaire did not reveal significant differences between pilo and the placebo. There were no serious or severe TEAEs and no TEAE-related discontinuations. The most common ocular TEAEs were headache and visual impairment with pilo (both 27.9%), and dry eye (7.0%) with the placebo. Conclusion The overall performance of night driving was inferior with pilo, compared with placebo. The study findings are consistent with the current class labelling and provide evidence to inform regulators and assist clinicians considering prescribing pilo to adults who seek treatment of presbyopia symptoms and drive at night. ClinicalTrials.gov identifier: NCT04837482.

Vehicle classification based on audio-visual feature fusion with low-quality images and noise

In Intelligent Transport Systems (ITS), vision is the primary mode of perception. However, vehicle images captured by low-cost traffic cameras under challenging weather conditions often suffer from poor resolution and insufficient detail representation. On the other hand, vehicle noise provides complementary auditory features that offer advantages such as environmental adaptability and a large recognition distance. To address these limitations and enhance the accuracy of low-quality traffic surveillance classification and identification, an effective audio-visual feature fusion method is crucial. This paper presents a research study that establishes an Urban Road Vehicle Audio-visual (URVAV) dataset specifically designed for low-quality images and noise recorded in complex weather conditions. For low-quality vehicle image classification, the paper proposes a simple Convolutional Neural Network (CNN)-based model called Low-quality Vehicle Images Net (LVINet). Additionally, to further enhance classification accuracy, a spatial channel attention-based audio-visual feature fusion method is introduced. This method converts one-dimensional acoustic features into a two-dimensional audio Mel-spectrogram, allowing for the fusion of auditory and visual features. By leveraging the high correlation between these features, the representation of vehicle characteristics is effectively enhanced. Experimental results demonstrate that LVINet achieves a classification accuracy of 93.62% with reduced parameter count compared to existing CNN models. Furthermore, the proposed audio-visual feature fusion method improves classification accuracy by 7.02% and 4.33% when compared to using single audio or visual features alone, respectively.

Are conversation and whispered voice hearing tests comparable to pure tone audiometry for assessment of hearing loss? A retrospective analysis

BackgroundPure tone audiometry (PTA) is considered the standard method of hearing assessment. However, technical and logistical challenges preclude its usage for hearing screening at primary healthcare facilities. Free field hearing (FFH) tests such as conversation voice (CV) and whispered voice (WV) tests have been used for hearing assessment. However, their correlation to PTA and accuracy in detection of hearing loss are doubtful. This study aims to determine if FFH tests can be used for estimation of hearing thresholds. MethodsA retrospective analytical study was conducted at the otorhinolaryngology department. Spearman's correlation coefficient (ρ) was determined between FFH recognition distance and average air conduction PTA thresholds. A statistical regression model was developed to estimate hearing thresholds from the FFH recognition distance. The FFH tests were studied for their accuracy in detection of hearing loss. ResultsThe audiometric records of 437 persons (874 ears) were analysed. The CV and WV tests were found to have a high correlation (ρ = −0.757 and −0.758, respectively) with average PTA thresholds in persons with hearing loss. The WV test was found to have a 94.7% sensitivity, 90.8% specificity, 97.6% positive predictive value, 80.9% negative predictive value, and 93.9% accuracy in detection of hearing loss. ConclusionFFH tests such as WV test can be used to estimate the hearing thresholds and screen for hearing loss when audiometric tests are not logistically feasible, such as at primary-level healthcare facilities, remote locations, and in schools. However, such tests should not be regarded as a substitute to PTA.

Vehicle-mounted imaging lidar with nonuniform distribution of instantaneous field of view

Imaging lidar is an important sensor for use in vehicle environment perception systems, and its field of view (FOV) and instantaneous field of view (IFOV) determine the system’s spatial coverage and the spatial resolution of detection, respectively. A smaller IFOV indicates that lidar can recognize a target over a greater distance. The FOV and the IFOV of conventional array-type lidar systems are constrained by the detector and cannot increase their target recognition distance while maintaining the same spatial coverage. To solve this problem, an imaging vehicle-mounted lidar system with a nonuniform IFOV distribution is proposed. By reducing the central IFOV while maintaining the same detection space, the system effectively doubles the available range for target discovery and recognition. Simultaneously, the central FOV has a larger entrance pupil, which matches the characteristic required to allow the central FOV to detect targets at greater distances. The proposed lidar system can recognize vehicle information at a longer range than conventional systems, and can play an important role in the 3D environmental perception field for intelligent vehicles.

Intelligent vision for the detection of chemistry glassware toward AI robotic chemists

One of the key steps to make an artificially intelligent (AI) and robotic chemist is the introduction of machine vision for guiding the experiment operation in the AI-redefined laboratory. In order to realize the targets, the prerequisites are to innovate/implement the intelligent vision for the detection of chemistry glassware. Here, we reported a computer vision method based on You only look once (YOLO) with a self-developed Chemical Vessel Identification Dataset (CViG) for the improvement of classification and recognition performance. The training dataset has been collected that includes 4072 images in real-time chemical laboratory. Three models, YOLOv5s, Slim-YOLOv5s and YOLOv7, have been exploited for the recognition of seven types of glassware in the condition of different scenarios (recognition distance, light and dark, stationary and moving). The improved Slim-YOLOv5s exhibited better recognition ability in various scenes, and the recognition accuracy of chemical vessels is improved by 1.51 % compared with YOLOv5s, and the size of the model is reduced from 14.4 MB to 11.0 MB. Slim-YOLOv5s's mAP is similar to YOLOv7's ability with a disadvantage of large volume, suggested that the improved Slim-YOLOv5s clearly has more advantages in terms of embedded requirements. This vision-assisted system capable of classifying chemical containers accurately in the scenarios of real-time chemical experiments will provide a good vision solution in the frontier fields of automated machine chemistry.

Research Method of Discontinuous-Gait Image Recognition Based on Human Skeleton Keypoint Extraction.

As a biological characteristic, gait uses the posture characteristics of human walking for identification, which has the advantages of a long recognition distance and no requirement for the cooperation of subjects. This paper proposes a research method for recognising gait images at the frame level, even in cases of discontinuity, based on human keypoint extraction. In order to reduce the dependence of the network on the temporal characteristics of the image sequence during the training process, a discontinuous frame screening module is added to the front end of the gait feature extraction network, to restrict the image information input to the network. Gait feature extraction adds a cross-stage partial connection (CSP) structure to the spatial-temporal graph convolutional networks' bottleneck structure in the ResGCN network, to effectively filter interference information. It also inserts XBNBlock, on the basis of the CSP structure, to reduce estimation caused by network layer deepening and small-batch-size training. The experimental results of our model on the gait dataset CASIA-B achieve an average recognition accuracy of 79.5%. The proposed method can also achieve 78.1% accuracy on the CASIA-B sample, after training with a limited number of image frames, which means that the model is more robust.

A novel method for Jinnan cattle individual classification based on deep mutual learning

As the core technology of precision animal husbandry, efficient and rapid identification of Jinnan cattle individuals can promote the scale, informatization and refinement of breeding, which is very necessary for the development of animal husbandry at this stage. However, the traditional livestock individual recognition method based on ear tag is labour-consuming, time-consuming, inefficient, easy to wear and limited by the recognition distance, and the accuracy is also very low. In order to solve this problem, a new method of Jinnan cattle individual recognition based on deep mutual learning is proposed by using the non-contact image recognition method. Two student networks are designed. They supervise each other and complete the task together. Their efficiency can be higher than that of a strong teacher network. Through this method, the individual recognition performance of Jinnan cattle is also enhanced. The experimental results verify the effectiveness of the method of deep mutual learning. Consult and learn from the peer network are used to improve generalization, so as to improve model recognition performance. Finally, the accuracy is 99.3% on the Jinnan cattle individual dataset established in this paper. The application of the contactless cattle individual recognition method in the farm is of great significance.

A Design Method for Controlling the Safe Distance of Convolution Image Recognition Based on FPGA

This is an FPGA-based image recognition system. It is built on the FPGA-SOC platform, which uses less power and operates more quickly. This method can be used to remind people inside to maintain a safe distance to stop the coronavirus from spreading. The system can ensure a high accuracy rate and identification speed. At the same time, the system detects the safe distance and issues pertinent prompts according to the training of the private data set. Finally, the system’s implementation is appropriate for tiny IoT devices with extremely low power consumption. The system is implemented by utilizing a custom CPU on an FPGA. The RISC-V architecture used in the self-developed CPU allows for 32-bit embedded CPU performance and operating effectiveness at the expense of an 8-bit CPU. An HD camera and an infrared camera that work together are put on it. The upgraded Tiny YOLO2 algorithm in the CPU can help the infrared camera process the HD camera’s data and perform recognition and judgments. Finally, it is possible to implement data collection based on 720p resolution, which precisely determines whether the direct distance between individuals is a safe distance and then sends the appropriate reminders. Its power usage is 1.3 W. It can address the issue of preventing epidemics and the security risks brought on by a large population density in public areas.

Target recognition and tracking system based on UAV platform

Machine learning can extract the features of the target from the known image dataset and detect the target quickly by using the trained deep neural network. Due to UAVs’ fast flight speed and long detection distance, real-time and accurate target detection requirements are relatively high. For such scenarios,a ground object recognition and tracking method that integrates the YOLOv5 target detection algorithm and KCF target tracking algorithm are proposed. The combination of the two algorithms can meet the requirements of recognition speed, recognition rate, and tracking accuracy in the current scene. Two algorithms are run simultaneously in the recognition process, the detection frame with the highest confidence is selected as the final detection result, and the detected target is tracked by the KCF target tracking algorithm based on correlation filtering. The experimental results show that this method not only meets the requirements of recognition distance, recognition accuracy, and recognition speed of ground target but also can track a ground target in real-time, with strong robustness and real-time performance.

Making night-time pedestrians safer using innovative clothing designs

Night-time pedestrians, including recreational walkers and runners, remain at significant risk of being killed or seriously injured, because drivers often fail to see them in time to avoid a collision. Clothing incorporating retro-reflective markers on the moveable joints creates a visual perception of biological motion or “biomotion” which has been shown to improve night-time pedestrian conspicuity. This study investigated whether clothing with markings in the biomotion configuration retains its conspicuity if adapted to include thinner or patterned retro-reflective strips making it more acceptable to wear for recreational pedestrians. In a night-time closed road study, the relative conspicuity of pedestrians to 14 young drivers with normal vision (mean age 24.3 ± 3.1 years) was assessed for pedestrians wearing different thicknesses and patterns of biomotion strips and compared with typical clothing worn by night-time pedestrians, including commercially available sports clothing with retro-reflective elements, a fluorescent yellow top, and black clothing. Results showed that all the biomotion configurations resulted in significantly longer recognition distances: around 2 times longer than the sports clothing, 2.5 times longer than the fluorescent top and 4 times longer than the black clothing. Similar trends were found for the distance at which drivers correctly identified pedestrian orientation (towards or sideways to the vehicle), with significantly longer distances for all the biomotion configurations, when compared to the sports, fluorescent top and black clothing. These findings highlight the effectiveness of biomotion clothing, even when using retro-reflective strips as thin as 0.75 cm and have implications for clothing designs for recreational walkers and runners to enhance their night-time safety.