Road Area Research Articles

Two-Dimensional Hydrodynamic Simulation of the Effect of Stormwater Inlet Blockage on Urban Waterlogging

The drainage capacity of stormwater inlets, which serve as the connection between the surface and the underground drainage system, directly affects surface runoff and the drainage capacity of underground drainage systems. However, in reality, stormwater inlets are often blocked due to the accumulation of leaves, human waste disposal and other factors, resulting in a greatly reduced drainage capacity of the drainage network and, in turn, urban waterlogging disasters. In view of the problem of stormwater inlet blockage, employing a typical waterlogging point in the Lianjiang Middle Road area of Fuzhou city as the research object, the stormwater inlet equivalent drainage method was adopted in this paper to characterize the drainage capacity of the pipe network and enable the control of the stormwater inlet blockage state. Coupled with the stormwater inlet drainage equation, an improved ITF-FLOOD two-dimensional hydrodynamic model was constructed, and the influence of stormwater inlet blockage on urban waterlogging under different rainfall return periods was simulated and analyzed. With increasing rainfall return period, the influences of stormwater inlet blockage on both the maximum area and the depth of accumulated water in the study area gradually decreased compared with those of a nonblocked stormwater inlet, and the growth proportions decreased from 43.35% and 34.58% under the 1-year rainfall scenario to 3.34% and 9.76% under the 50-year rainfall scenario, respectively. However, in terms of the change in the accumulated water level, stormwater inlet blockage will cause an increase, and the influence will always be significant. Overall, stormwater inlet blockage aggravated the waterlogging risk and the extent of waterlogging. Therefore, the results provided a reference for the construction of an urban waterlogging model and have certain guiding significance for waterlogging prevention and control in the study area prone to stormwater inlet blockage.

Monocular Vision Sensor-Based Indoor Road and Stair Detection

Road detection is an essential component of indoor robot navigation. Vision sensors have great advantages in road detection as they can provide rich information in terms of environmental perception. In this paper, a monocular vision sensor-based method for indoor road and stair detection is proposed, which detects feasible areas in indoor environments very fast without paying attention to detailed features of walls or other obstacles. More specifically, for a given indoor road image captured by an on-board vision sensor, the simple linear iterative clustering (SLIC) algorithm-based approach for efficient image segmentation is introduced. Then, according to the DBSCAN algorithm, the generated superpixels are clustered to form large areas of view. The initial road area is obtained through a safe window on the middle bottom of the image. In order to achieve a more accurate road segmentation, the initial image is processed by the binary search, edge detection based on the Canny operator and straight-line detection and location based on the Hough transform, which integrates edge and stair information into road detection. Several experiments are performed to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed method could accurately detect road information and staircase information in images and succeeds in addressing the indoor road-detection problem.

Strategy for the Development of the Street Area Hygiene Sanitation Standards in Astanaanyar Market Bandung

Bandung, as one of the most popular tourism destinations in West Java, offers unique cultural, natural and culinary treasures. However, culinary tourism in Bandung City faces challenges such as the problem of implementing sanitary hygiene, especially for street vendors. This study aims to create a strategy for the development of a street food area with sanitary hygiene standards in Pasar Astanaanyar, Bandung City, focusing on the availability of sanitation facilities and knowledge of food handlers, from the data on facilities and knowledge obtained, appropriate strategies will be formulated to be implemented. The method in this study uses a Likert scale and a multiple choice statement test given to street vendors by analyzing the data descriptively. The results obtained that the sanitation hygiene facilities in Astanaanyar market are categorized as not good with the largest result more than half (87.5%) of traders do not have sewerage facilities, but the knowledge possessed by traders is categorized as quite good regarding personal hygiene , food hygiene and sanitation facilities with the largest average score of 68.75 regarding food hygiene. The conclusion of this study is the need for involvement between the government, the private sector, and the street vendors themselves to develop a hygienic sanitation standardized street vendor area.

Evaluation of sand subgrade seepage erosion caused by buried pipeline leakage

Leakage from buried pipelines can lead to an increase in the water content of the subgrade soils and a rise in the water table, leading to soil loosening, erosion and ultimately the formation of hidden voids and roadway collapses. This study presents a discrete-element method and validates its accuracy by utilising cavity data from model experiments. It investigates the mechanism of seepage erosion resulting from pipe leakage and analyses the development of the soil arch effect. Furthermore, it discusses the influence of sand void ratio and particle size on sand seepage erosion. The results indicate that the erosion area is primarily affected by the void ratio and particle size. In comparison to soil particles ranging from 0.1 to 5 mm and from 2 to 5 mm, those with sizes between 0.1 mm and 2 mm generate areas of erosion and loosening that are approximately 40% larger. The proposed model offers a precise analysis of the developmental process and the extent of seepage erosion, thereby contributing to the prediction of potential road cavity areas based on dynamic changes in key factors such as subgrade soil type and groundwater level.

Practical aspects of measuring camera‐based indicators of alcohol intoxication in manual and automated driving

AbstractCamera‐based Driver Monitoring Systems (DMS) have the potential to exploit eye tracking correlates of alcohol intoxication to detect drunk driving. This study investigates how glance, blink, saccade, and fixation metrics are affected by alcohol, and whether possible effects remain stable across three different camera setups, as well as when the driver is out‐of‐the‐loop during level 4 automated driving (Wizard‐of‐Oz setup). Thirty‐five participants drove on a test track first sober and then with increasing intoxication levels reaching a breath alcohol concentration (BrAC) of 1‰. Linear Mixed‐Effects Regression analyses showed that with increasing intoxication levels, eye blinks became longer and slower, glances and fixations became fewer and longer, and more attention was directed to the road area, at the expense of more peripheral areas. Fixation and blink metrics were more robust to changes in automation mode, whereas glance‐based metrics were highly context dependent. Not all effects of alcohol intoxication could be measured with all eye tracking setups, where one‐camera systems showed lower data availability and higher noise levels compared to a five‐camera system. This means that lab findings based on higher quality eye tracking data might not be directly applied to production settings because of hardware limitations.

Functional connectivity related to road linear erosion at rainfall event scale in an agricultural watershed on the Loess Plateau

Intersections of road and channel networks assist in better understanding of runoff and sediment transportation process feedback using the hydrological connectivity concept. However, quantifying functional connectivity and exploring the complex relationship between hydrological connectivity and the processes of runoff and sediment associated with road networks have largely been unexamined by field work. Therefore, our team conducted a 3-year monitoring of hydrological connectivity and road erosion from selected road segments in an agricultural watershed on the Loess Plateau. Rainfall events occurring during 2020–2022 were categorized into three types using K-means clustering analysis: small amounts of low-intensity rainfall (SL-R), large amounts of low-intensity rainfall (LL-R), and large amounts of high-intensity rainfall (LH-R). The monitored roads primarily consist of paved roads and unpaved roads (agricultural roads, abandoned roads, and petroleum roads). In our study, the intersections extracted by the road and channel networks were defined as hydrological connections, and sticks wrapped with clean gauze at the hydrological connections were monitored under variable rainfall conditions for functional connectivity assessment. The runoff heights and sediment traces attached to the sticks are alternative hydrological connectivity indicators. The results revealed an obvious increasing trend in the activation frequency and degree of hydrological connectivity as rainfall types transitioned from SL-R to LL-R and LH-R. Agricultural roads had a more intense response to hydrological connectivity between upslope drainage areas and road surfaces, and petroleum roads performed well in response to hydrological connectivity between road surfaces and down hillslopes. Random forest models indicate that PI30 is the most dominant influencing factor of road erosion and hydrological connectivity between road upslope drainage areas and road surfaces. Road surface erosion rates are the primary determinants of hydrological connectivity between road surfaces and down hillslopes. These findings suggest a close interaction between road erosion and hydrological connectivity driven by these off-site effects. They offer additional evidence concerning runoff and sediment patterns and dynamics in relation to road networks and identify hydrological connectivity and road erosion hotspots. Coordinating measures for hydrological connectivity regulation and road erosion mitigation are essential for minimizing hydrological connectivity and road erosion off-site effects within watersheds.

Detection and Classification of Lanes Using Reannotated Lane Dataset to Aid Autonomous Driving

The core functionality of advanced driver assistance systems and self-driving cars depends on the ability to recognize drivable road areas. As there are many different categories of useful markings incorporated within the road area, lane detection and classification form a major step for taking appropriate actions leading to truly autonomous driving. Existing datasets do not provide ample classification of lane types and adequate granularity for precise localization of lane markings. A new dedicated dataset for semantic segmentation of 11 varied lane types obtained by reannotating the BDD100K dataset is presented. The reannotation process involves pixel-level lane markings of 76,000 image instances of the BDD100K dataset, which were originally represented as a sequence of coordinate points. This opens up the possibility of high resolution both spatially and semantically in the context of lane understanding for autonomous driving. Baseline results on the proposed dataset based on the Bilateral Segmentation Network (BiSeNetV2), which considers the spatial data and the categorical semantics distinctly, are presented. The performance is pinned at 85% accuracy during testing using BiSeNetV2 architecture. The dataset is expected to open up new directions of research to address problems such as severe class imbalance, segmentation of multiple classes of texture-less and eccentric features (lanes), and so forth. As a result, applications such as lane-centric activity interpretation, future event prediction, and continuous learning are expected.

The Study of Spatial Attributes of the Wat Ket - Fah Ham - San Pa Koi Connected Area, Chiang Mai, to Identity Characteristics of the Cultural Landscape

This research article aims to explore the spatial components of the Wat Ket, Fah Ham, and San Pa Koi connected areas in Chiang Mai and analyze their roles in defining the identity of the cultural landscape. The research problem is to investigate how exploring spatial components that define the cultural landscape identity in the study area will yield results and how the roles and importance can be identified. The researcher uses qualitative research methods focused on field surveys, cultural landscape surveys, and unstructured interviews as tools for data collection. The sample groups include four selected communities within the district and a group of 44 informants. Subsequently, physical data analysis and interview information are used and presented through descriptions, maps, and illustrations. The research findings indicate that the spatial components defining the cultural landscape identity within the study area can be categorized into nine groups, including open spaces for recreation in the Baan San Pa Khoi community, open spaces along the Ping River near Nawarat Bridge, commercial areas in the San Pa Khoi Market, commercial areas on Kaew Nawarat Road, educational and hospital areas, commercial areas on Charoen Mueang Road, commercial areas on Bumrungrad Road, commercial areas along the Ping River in the area of the 1st Church, Chiang Mai (old building), commercial areas along the Ping River in the Wat Ket area, and the McCormick Hospital area. In terms of analyzing the roles in defining the identity of the cultural landscape, they can be categorized into five roles, including historical and cultural areas, commerce and economic activity areas, architecture and community areas, religious and spiritual areas, and public areas.

Vulnerability to ARI and Diarrhea after Disaster

This research was conducted in shelters in two sub-districts, namely Mantikulore and Ulujadi sub-districts, with the aim of describing the level of ARI and diarrhea vulnerability after the disaster (especially shelters in Mantikulore and Ulujadi sub-districts). This research uses weighting scoring method and image interpretation method. The population in this study was 1,138 families from temporary shelters in the two sub-districts, with a sample size of 114 families. Meanwhile, the technique used to obtain samples is quato sampling. The results obtained are (1) In the parameter of ARI sufferers and the parameter of the use of anti-mosquito coils, it can be seen that most respondents fall into the classification of the class not vulnerable. On the parameter of the physical environment of the shelter (number of shelter members and ventilation area), all shelters are classified as highly vulnerable. On the smoking habit parameter, it can be seen that most respondents fall into the highly vulnerable class classification. In the parameter of road area, it can be seen that there are some shelters that are classified as not vulnerable and some are classified as very vulnerable. (2) In the parameter of ARI patients, it can be seen that most respondents are classified as not vulnerable. In the parameter of hand washing before eating and after defecation, it can be seen that all respondents are classified as not vulnerable. For waste management, half of the respondents were classified as vulnerable. In terms of the distance from the shelter to the polling station, it can be seen that all shelters are classified as highly vulnerable.

An Assessment of Urban Residential Environment Quality Based on Multi-Source Geospatial Data: A Case Study of Beijing, China

Assessing the urban residential environment quality (REQ) is essential for advancing sustainable urban development and enhancing urban residents’ living standards. Traditional REQ assessments rely on statistical data, prone to delays and lacking holistic insight. This study takes residential blocks as the analysis units and is conducted within the area of the Sixth Ring Road in Beijing. It synthesizes multi-source geospatial data to devise a comprehensive framework for assessing urban REQ, incorporating facets of environmental health and comfort, housing comfort, transportation convenience, city security, and life convenience. Utilizing the principle of minimal relative informational entropy, this study integrates the Analytic Hierarchy Process (AHP) with the entropy method to determine the weight of each evaluative criterion. Subsequently, a linear weighting technique is employed to ascertain the scores for each evaluative criterion, thus facilitating a detailed examination of the REQ. Finally, the research probes into the complex interrelation between the assessed REQ and the city’s Gross Domestic Product (GDP) and carbon emissions across varying scales. Findings reveal that (1) the overall REQ within Beijing’s Sixth Ring Road is superior at the center and diminishes towards the periphery. (2) The dispersion of environmental health and comfort and city security metrics is relatively uniform, showing minor variations; however, a marked disparity is observed in the distribution of housing comfort metrics. (3) Regions characterized by higher GDP tend to demonstrate relatively higher levels of the REQ. Conversely, areas boasting higher-quality urban REQ are more inclined to exhibit increased levels of carbon emissions.

The morphology of urban tourism space (case: Malioboro Main Street as cosmological Axis of Yogyakarta city, Indonesia)

Purpose This study aims to analyze the morphological landscape of the urban tourism spaces in the Malioboro Main Street area to predict its future development and to assist in creating effective regional planning policies. Design/methodology/approach This study uses qualitative grounded theory, typo morphological analysis and place-centered mapping techniques. Findings From the observation, it was found that each element of the city image area has different components of the urban tourism space’s morphology. The urban tourism spaces in Malioboro Main Street are distributed in a centered, linear and scattered pattern, with zones dedicated to shopping, cultural experiences and cuisine. The specific elements of each zone, such as attractions, accommodations or amenities, determine the type of tourism offered, such as cultural, shopping and culinary tourism. Research limitations/implications The study presented here may serve as a helpful guide for regulating and organizing policies that can form a sound basis for policy formulation. It mainly pertains to planning and structuring the Malioboro Main Street area, which currently needs a well-conceived urban tourism landscape development concept. Practical implications This study will have practical implications in projecting tourism development in the area through planning and structuring. The study can also be used as a basis and guideline for controlling, organizing and determining regulations and policies, ultimately serving as a wise foundation for policy formulation, particularly in planning and structuring the Cosmological Axis area, especially the Malioboro area. Originality/value The discussion of morphology is mainly limited to macro or micro scales, while the discussion of tourism space is generally limited to conceptual development. Conversely, this study aims to explore the unique landscape morphology of the Malioboro Main Street area, which serves as evidence of a productive space for urban tourism by typomorphological analysis through the approach of urban landscape elements, tourism elements and its activities. Thus, it may complement previous studies in urban areas dominated by tourism.

Research on LiDAR point cloud data transformation method based on weighted altitude difference map

Road surface detection plays a pivotal role in the realm of autonomous vehicle navigation. Contemporary methodologies primarily leverage LiDAR for acquiring three-dimensional data and utilize imagery for chromatic information. However, these approaches encounter significant integration challenges, particularly due to the inherently unstructured nature of 3D point clouds. Addressing this, our novel algorithm, specifically tailored for predicting drivable areas, synergistically combines LiDAR point clouds with bidimensional imagery. Initially, it constructs an altitude discrepancy map via LiDAR, capitalizing on the height uniformity characteristic of planar road surfaces. Subsequently, we introduce an innovative and more efficacious attention mechanism, streamlined for image feature extraction. This mechanism employs adaptive weighting coefficients for the amalgamation of the altitude disparity imagery and two-dimensional image features, thereby facilitating road area delineation within a semantic segmentation framework. Empirical evaluations conducted using the KITTI dataset underscore our methodology’s superior road surface discernment and extraction precision, substantiating the efficacy of our proposed network architecture and data processing paradigms. This research endeavor seeks to propel the advancement of three-dimensional perception technology in the autonomous driving domain.

Snow‐ or ice‐covered road detection in winter road surface conditions using deep neural networks

AbstractTraffic accidents occur frequently in cold and snow‐ or ice‐covered regions due to weather changes that occur during the winter season. To detect the snow‐ or ice‐covered roads in road surface conditions, road surface images captured using fixed‐point cameras installed along the route are sufficient. This paper proposes a snow‐ or ice‐covered road detection method that uses the deep convolutional autoencoding Gaussian mixture model (DCAGMM) with structural similarity (SSIM). The DCAGMM method, which is an unsupervised anomaly detection method, is unaffected by imbalance in the training data. In addition, the end‐to‐end convolutional neural network implemented in the DCAGMM enables the capture of the unique characteristics of the road surface images. Finally, by reconstructing the input images as normal images, the comparison of the input and reconstructed images enables identification of snow‐ or ice‐covered road areas without requiring pixel‐level annotations. Furthermore, the road surface images include complex characteristics for reconstruction, and the SSIM‐based reconstruction error allows us to preserve the image quality of the reconstructed image. Experimental results obtained on real‐world road surface images demonstrate the effectiveness of the proposed method.

A Novel Network Framework on Simultaneous Road Segmentation and Vehicle Detection for UAV Aerial Traffic Images.

Unmanned Aerial Vehicle (UAV) aerial sensors are an important means of collecting ground image data. Through the road segmentation and vehicle detection of drivable areas in UAV aerial images, they can be applied to monitoring roads, traffic flow detection, traffic management, etc. As well, they can be integrated with intelligent transportation systems to support the related work of transportation departments. Existing algorithms only realize a single task, while intelligent transportation requires the simultaneous processing of multiple tasks, which cannot meet complex practical needs. However, UAV aerial images have the characteristics of variable road scenes, a large number of small targets, and dense vehicles, which make it difficult to complete the tasks. In response to these issues, we propose to implement road segmentation and on-road vehicle detection tasks in the same framework for UAV aerial images, and we conduct experiments on a self-constructed dataset based on the DroneVehicle dataset. For road segmentation, we propose a new algorithm C-DeepLabV3+. The new algorithm introduces the coordinate attention (CA) module, which can obtain more accurate segmentation target location information and make the segmentation target edges more continuous. Also, the improved algorithm introduces the cascade feature fusion module to prevent the loss of detail information in road segmentation and to obtain better segmentation performance. For vehicle detection, we propose an improved algorithm S-YOLOv5 by adding a parameter-free lightweight attention module SimAM. Finally, the proposed road segmentation-vehicle detection framework is utilized to unite the C-DeepLabV3+ and S-YOLOv5 algorithms for the implementation of the serial tasks. The experimental results show that on the constructed ViDroneVehicle dataset, the C-DeepLabV3+ algorithm has an mPA value of 98.75% and an mIoU value of 97.53%, which can better segment the road area and solve the problem of occlusion. The mAP value of the S-YOLOv5 algorithm has an mAP value of 97.40%, which is more than YOLOv5's 96.95%, which effectively reduces the vehicle omission and false detection rates. By comparison, the results of both algorithms are superior to multiple state-of-the-art methods. The overall framework proposed in this paper has superior performance and is capable of realizing high-quality and high-precision road segmentation and vehicle detection from UAV aerial images.

Art Deco Architecture in Pune from the 1940s to the 1960s

Art Deco is a visual arts, architecture, and product design style. It first emerged in Paris in the 1910s, shortly before World War I. This style reached its height in Europe and the United States in the 1920s and early 1930s. The early to mid-1930s saw the emergence of Art Deco architecture in India, which ended in the mid to late 1950s. Indian cities like Mumbai, Pune, Chennai, Jaipur, Jodhpur, and Patiala are home to Art Deco structures. In terms of architecture, the Art Deco movement represented a dramatic departure from the colonial Neo-Gothic/Victorian Gothic model. The pursuit of modernity, multiculturalism, and cosmopolitanism were all reflected in Art Deco architecture. UNESCO declared the Victorian Gothic and Art Deco ensembles of Mumbai (Maharashtra’s capital) a UNESCO World Heritage Site in June 2018. Nevertheless, Pune City’s Art Deco buildings are not recognized as heritage buildings or documented. The study involved photographic documentation of Art Deco buildings in Deccan, Swargate Prabhat Road, Bhandarkar Road area of Pune. This paper examines the various motifs and their underlying symbolism that can be seen in Pune’s Art Deco buildings. The results of this study show that the Art Deco buildings in Pune are now on the verge of extinction due to the age of the building, structural deformities and lack of awareness of the conservation of this historical heritage. The art deco features on the elevations of many buildings have been hidden/erased by renovation and commercial hoardings.

Computational Fluid Dynamics Simulation of High-Resolution Spatial Distribution of Sensible Heat Fluxes in Building-Congested Area

Urban areas consist of various land cover types, with a high proportion of artificial surfaces among them. This leads to unfavorable thermal environments in urban areas. Continuous research on the thermal environment, specifically on the sensible heat flux (Qh), has been conducted. However, previous research has faced temporal, spatial, and resolution limitations when it comes to detailed analysis of sensible heat flux in urban areas. Therefore, in this study, a computational fluid dynamics (CFD) model combined with the LDAPS and the VUCM was developed to simulate Qh at one-hour intervals over a 1-month period in an urban area with various land cover types. Model validation was performed by comparing it with measurements, confirming the suitability of the model for simulating Qh. The land cover was categorized into five types: building, road, bare land, grassland, and tree areas. Qh exhibited distinct patterns depending on the land cover type. When averaging the Qh distribution over the target period, buildings, roads, and bare land areas showed a predominance of upward Qh values, while grassland and tree areas displayed dominant downward Qh values. Additionally, even within the same land cover types, slight Qh variations were identified based on their surroundings. The averaged Qh value for building areas was the highest at 36.79 W m−2, while that for tree areas was −3.04 W m−2. Moreover, during the target period, the time-averaged Qh showed that building, road, and bare land areas peaked at 14 LST, while grassland and tree areas exhibited very low Qh values. Notably, buildings reached a maximum Qh of 103.30 W m−2 but dropped to a minimum of 1.14 W m−2 at 5 LST.

Analysis of landslide-prone areas in Tripe Jaya district, Gayo Lues Regency

Abstract Tripe Jaya District is one of the sub-districts of Gayo Lues regency, which has hilly and mountainous morphological areas with steep slopes and landslides in several regions of the road area. This study aims to identify and map the location of landslides and analyze landslide areas. A quantitative method for measuring landslide-prone levels from the Center for Volcanology and Geological Hazard Mitigation 2005, the method used is the score and weight of the parameters that cause landslide-prone. The causes of landslide-prone sites are rainfall data, soil type, elevation, land use, and slope. The results of this study have five classes of landslide vulnerability, including very low landslide vulnerability level covering an area of 279.21 Ha (1%), low vulnerability level covering an area of 3639.80 Ha (9%), medium vulnerability level covering an area of 17893.07 Ha (43%), high vulnerability level covering an area of 19822.91 Ha (47%), and very high vulnerability level covering an area of 150.23 Ha (0.36%). Landslide events in Tripe Jaya District based on field observations, there are landslide events in high and very high classes. This research provides an overview of landslide-prone areas so the results of the study can be used as a basis for planning mitigation strategies and as a consideration for the government in building the required mitigation developing mitigation measures related to landslide-prone risks in the Tripe Jaya.

Emission Factors of Tyre Wear Particles Emitted by Light Road Vehicles in Real Driving Conditions: A New Challenge for Clean Road Transport to Improve Urban Air Quality

Non-exhaust road transport emissions in cities contribute to poor air quality and have an impact on human health. This paper presents a new study of particles emitted by tyre wear in real driving conditions and gives their emission factors. The most frequently emitted particles were collected in urban, suburban and road areas. They were identified and analysed physically and chemically. Their level of toxicity is well known. An overall analysis of the measured pollutants was carried out to assess their emission factors in real driving situations. The highest emitting pollutants, considered separately, were found to have high emission factors. The values obtained exceed the Euro standard for vehicles but are below those of vehicles not equipped with particle filters. Significant test analysis confirmed that the inertia of chemical pollutants is homogeneous. Emission factors have also been provided for PM10 and PM2.5. These results should contribute to the emergence of future regulations of non-exhaust emissions and should help to analyse the exposure-impact relationship for particles from tyre wear.

Child Missing with Surveillance Analysis with Multi Phase Extraction Face Monitoring

Abstract: Visual Surveillance has been placed in everywhere for any incidents identification. With the wide developments missing child identification with the computer vision system is the main focus. The implementation carried out with face based detection with rapid advance digital connectivity. Face recognition plays as a major developmental process where the concern person can be identified with face based identification system. Machine learning with artificial intelligence plays a vital role in facial detection and recognition. The paper proposes Haar Cascade on feature extraction from facial points and Hybrid Recurrent Neural System(HRNN) with Long Short Term Memory(LSTM) algorithm, on multi face recognition over public crowded area. Missing child images will be pre- trained in a database where the face matching templates will be provided. On matching the database labeled points with the feature point an E-mail or SMS based alert system can be generated on notification to the authority with cam points. Thus the development carried will give best accuracy solution on finding out from multiple face regions. Mainly identification of the missing child over the road side areas will be done with computer vision frame targeting system.

Urban Texture Identification and Characteristic Analysis Based on Percolation Theory—A Case Study of the Second Ring Road Area in Wuhan City

The urban texture is the physical manifestation of the urban form’s evolution. In the rapid process of urbanization, protecting and reshaping the urban texture has become an essential means to sustain the overall form and vitality of cities. Previous studies in this field have primarily relied on image analysis or typological methods, lacking a quantitative approach to identify and analyze the urban texture on a large scale. Moreover, the hierarchical structure and networked organization of the urban texture are gradually being elucidated and emphasized. This study takes a complex network perspective and applies percolation theory to identify and analyze the urban texture. The hierarchical evolutionary characteristics of the urban fabric and the underlying mechanisms driving the scale-dependent differences are analyzed, and the toughness of the urban texture is evaluated based on hierarchical connections. The research findings reveal the key scale in the cross-scale evolution of urban textures, with variations in scale dependence and hierarchical evolution characteristics among different types of urban texture. The traditional urban texture displays sensitivity to scale changes, maintaining its integrity and internal connectivity at small scales. On the other hand, the texture characteristics of modern and contemporary urban areas are only discernible at larger scales. The emergence of large-scale texture clusters at specific scale levels can be explained using multiple index systems. This study has reference significance for the preservation and renewal of the urban fabric in urban renewal processes.