Intelligent Segmentation and Measurement Model for Asphalt Road Cracks Based on Modified Mask R-CNN Algorithm

Nowadays, asphalt road has dominated highways around the world. Among various defects of asphalt road, crackshave been paid more attention, since cracks often cause major engineering and personnel safety incidents. Currentmanual crack inspection methods are time-consuming and labor-intensive, and most segmentation methods cannot detect cracks at the pixel level. This paper proposes an intelligent segmentation and measurement model basedon the modified Mask R-CNN algorithm to automatically and accurately detect asphalt road cracks. The modelproposed in this paper mainly includes a convolutional neural network (CNN), an optimized region proposalnetwork (RPN), a region of interest (RoI) Align layer, a candidate area classification network and a Mask branch offully convolutional network (FCN). The ratio and size of anchors in the RPN are adjusted to improve the accuracyand efficiency of segmentation. Soft non-maximum suppression (Soft-NMS) algorithm is developed to improvethe segmentation accuracy. A dataset including 8,689 images (512× 512 pixels) of asphalt cracks is established andthe road crack is manually marked. Transfer learning is used to initialize the model parameters in the trainingprocess. To optimize the model training parameters, multiple comparison experiments are performed, and the testresults show that the mean average precision (mAP) value and F1-score of the optimal trained model are 0.952 and0.949. Subsequently, the robustness verification test and comparative test of the trained model are conducted andthe topological features of the crack are extracted. Then, the damage area, length and average width of the crackare measured automatically and accurately at pixel level. More importantly, this paper develops an automatic crackdetection platform for asphalt roads to automatically extract the number, area, length and average width of cracks,which can significantly improve the crack detection efficiency for the road maintenance industry.

An Intelligent Adaptive Dynamic Algorithm for a Smart Traffic System

Due to excessive car usage,pollution and traffic have increased.In urban cities in Saudi Arabia,such as Riyadh and Jeddah,drivers and air quality suffer from traffic congestion.Although the government has implemented numerous solutions to resolve this issue or reduce its effect on the environment and residents,it still exists and is getting worse.This paper proposes an intelligent,adaptive,practical,and feasible deep learning method for intelligent traffic control.It uses an Internet of Things(IoT)sensor,a camera,and a Convolutional Neural Network(CNN)tool to control traffic in real time.An image segmentation algorithm analyzes inputs from the cameras installed in designated areas.This study considered whether CNNs and IoT technologies could ensure smooth traffic flow in high-speed,high-congestion situations.The presented algorithm calculates traffic density and cars’speeds to determine which lane gets high priority first.A real case study has been conducted on MATLAB to verify and validate the results of this approach.This algorithm estimates the reduced average waiting time during the red light and the suggested time for the green and red lights.An assessment between some literature works and the presented algorithm is also provided.In contrast to traditional traffic management methods,this intelligent and adaptive algorithm reduces traffic congestion,automobile waiting times,and accidents.

Automation in road distress detection,diagnosis and treatment

Road transportation plays a crucial role in society and daily life,as the functioning and durability of roads can significantly impact a nation's economic development.In the whole life cycle of the road,the emergence of disease is unavoidable,so it is necessary to adopt relevant technical means to deal with the disease.This study comprehensively reviews the advancements in computer vision,artificial intelligence,and mobile robotics in the road domain and examines their progress and applications in road detection,diagnosis,and treatment,especially asphalt roads.Specifically,it analyzes the research progress in detecting and diagnosing surface and internal road distress and related techniques and algorithms are compared.In addition,also introduces various road gover-nance technologies,including automated repairs,intelligent construction,and path planning for crack sealing.Despite their proven effectiveness in detecting road distress,analyzing diagnoses,and planning maintenance,these technologies still confront challenges in data collection,parameter optimization,model portability,system accuracy,robustness,and real-time performance.Consequently,the integration of multidisciplinary technologies is imperative to enable the development of an integrated approach that includes road detection,diagnosis,and treatment.This paper addresses the challenges of precise defect detection,condition assessment,and unmanned construction.At the same time,the efficiency of labor liberation and road maintenance is achieved,and the automation level of the road engineering industry is improved.

Short-Term Traffic Flow Prediction Based on Road Network Topology

Accurate short-term traffic flow prediction plays a crucial role in intelligent transportation system (ITS), because it can assist both traffic authorities and individual travelers make better decisions. Previous researches mostly focus on shallow traffic prediction models, which performances were unsatisfying since short-term traffic flow exhibits the characteristics of high nonlinearity, complexity and chaos. Taking the spatial and temporal correlations into consideration, a new traffic flow prediction method is proposed with the basis on the road network topology and gated recurrent unit (GRU). This method can help researchers without professional traffic knowledge extracting generic traffic flow features effectively and efficiently. Experiments are conducted by using real traffic flow data collected from the Caltrans Performance Measurement System (PEMS) database in San Diego and Oakland from June 15, 2017 to September 27, 2017. The results demonstrate that our method outperforms other traditional approaches in terms of mean absolute percentage error (MAPE), symmetric mean absolute percentage error (SMAPE) and root mean square error (RMSE).

Tire Road Friction Coefficient Estimation:Review and Research Perspectives

Many surveys on vehicle traffic safety have shown that the tire road friction coefficient(TRFC)is correlated with the probability of an accident.The probability of road accidents increases sharply on slippery road surfaces.Therefore,accurate knowledge of TRFC contributes to the optimization of driver maneuvers for further improving the safety of intelligent vehicles.A large number of researchers have employed different tools and proposed different algorithms to obtain TRFC.This work investigates these different methods that have been widely utilized to estimate TRFC.These methods are divided into three main categories:off-board sensors-based,vehicle dynamics-based,and data-driven-based methods.This review provides a comparative analysis of these methods and describes their strengths and weaknesses.Moreover,some future research directions regarding TRFC estimation are presented.

Roadroid： Continuous Road Condition Monitoring with Smart Phones

Road condition is an important variable to measure in order to decrease road and vehicle operating/maintenance costs, but also to increase ride comfort and traffic safety. By using the built-in vibration sensor in smart phones, it is possible to collect road roughness data which can be an indicator of road condition up to a level of Class 2 or 3 in a simple and cost efficient way. Since data collection therefore is possible to be done more frequently, one can better monitor roughness changes over time. The continuous data collection can also give early warnings of changes and damage, enable new ways to work in the operational road maintenance management, and can serve as a guide for more accurate surveys for strategic asset management and pavement planning. Collected measurement data are wirelessly transferred by the operator when needed via a web service to an internet mapping server with spatial filtering functions. The measured data can be aggregated in preferred sections, as well as exported to other GlS （geographical information systems） or road management systems. Our conclusion is that measuring roads with smart phones can provide an efficient, scalable, and cost-effective way for road organizations to deliver road condition data.

Traffic organization during urban road constructions

This research presented a bi-level programming approach to optimize the schedule of ur- ban road construction activities based on a hypothetical transport network, with an objective of mini- mizing the overall traffic delays. A heuristic algorithm was utilized to identify a set of road construction schedules, while PARAMICS was adopted to estimate the total travel time in the network under each road construction scenario. To test the performance of proposed heuristics-simulation methodology, a numerical test was implemented. The overall results suggested that the proposed methodol- ogy could quickly find the optimum solution with good convergence.

新时代“情报学+”数智化人才的培育理路

[目的/意义]数智化时代背景下,更要求情报学学科融入新时代,积极探索数智化人才培育路径。[研究设计/方法]从认知到应用全方位策划“情报学+”数智化人才的培育理路,明晰育人体系,重点描述人才核心素养模型的搭建以及对人才培育路径的思考,展现情报学育人的本土化、特色化、智慧化。[结论/发现]情报学学科未来应致力于培育服务新时代各工作领域和研究情境的复合应用型情报学人才,不断探索更完整的人才培育闭环。[创新/价值]以中国哲学思想赋能新时代情报人才培育的理论遵循和实践路径,体现哲学思想现实价值的同时,为情报学学科的未来发展提供参考。

Building of road boundary map based on laser radar

A method for road boundary detection and tracking using laser ladar with respect to a vehicle＇ s local coordinates is proposed. It can be applied to different types of road conditions, such as roads with or without curbs, having relatively rough road surface and with obstacles on road surface. In the method, some line segments are extracted after a series of preprocessing on range data. The extracted line segments are combined and further selected. They are then united to match the road models and generate the road boundary points which are tracked by Kalman filter. Then the obtained road boundary points are transformed to build a precise vector map by least squares fitting algorithm. These fitted line segments represent road boundary vectors. The vector map is precise enough to provide ample road information such as the orientation of road, the road width and the passable road region. Finally, extensive experiments conducted in urban and semi-urban environment demonstrate the robustness, effectiveness and viability of the proposed method.

区块链在城市道路智能交通中的应用综述

从安全、共享、运营三个方面介绍了区块链在城市道路智能交通中的应用。在安全方面,区块链的去中心化和不可篡改的特性能够保护数据信息安全和用户隐私安全;在共享方面,区块链的激励机制促进了车辆计算资源和交通信息的共享;在运营方面,区块链的不可篡改和可追溯性保证了运营中对交通违章准确有效的溯源,利用共识机制让交通信号灯配时更加高效。此外,分析了区块链在城市道路智能交通应用中存在的不足,并展望了区块链在城市道路智能交通领域的未来发展方向。

道路交通与生活场景诱发下驾驶人情绪的脑认知响应差异

为了探究道路交通场景和生活场景下驾驶人情绪脑认知响应差异,邀请10名被试分别在2种场景下完成情绪诱导实验。从离散情绪和连续情绪2个维度分析2种场景下前额与外额含氧血红蛋白和脱氧血红蛋白浓度的差异,进而推断脑区的激活程度。结果表明,道路交通场景相较于生活场景,愤怒与惊讶情绪下前额与外额的激活程度更强,快乐情绪下前额与外额的激活程度更低,恐惧情绪下仅在前额发现了显著差异,且激活程度更低;道路交通场景相较于生活场景,愤怒、恐惧、惊讶3种情绪前额与外额的脑功能连接强度更高,快乐与悲伤2种情绪下前额与外额的脑功能连接强度更低。

公路路基变形破坏及其测试技术研究进展与展望

路基变形是公路病害的主要原因之一,其在公路建设、管理、运营、养护过程中带来的隐患日益凸显。目前以遥感测绘、地球物理探测、多传感器为代表性的相关技术,在路基变形测试中发挥了重要作用。为研究道路工程领域动态和行业发展趋势,梳理近年来道路工程领域理论、方法、技术及装备研究现状,阐述了公路路基变形的主要形式及影响因素,分析了路基变形测试技术类型及其优缺点,进一步讨论了对全周期智慧监测体系的认识及其发展趋势。构建路基变形监测体系是感知路基状态、路基稳定性评估和灾害预警的主要途径。随着人工智能和先进测试技术的快速进步,路基变形测试技术呈现出动态化、透明化、智慧化等新特点。加强公路测试基础理论、技术与装备的研发,形成基于立体空间的多手段路基健康监测体系是未来道路安全运维的重要发展方向。

人工智能赋能低空经济:应用场景与未来方向

低空经济是指以各种有人驾驶和无人驾驶航空器在海拔3000米以下的各类飞行活动作为牵引,辐射带动相关领域融合发展的新兴经济形态和效益。2024年全国两会期间,“低空经济”被写入政府工作报告。报告提出,“积极培育新兴产业和未来产业”“积极打造生物制造、商业航天、低空经济等新增长引擎”。我们可以通过解析人工智能赋能低空经济、大数据驱动的部分应用场景(智慧城市建设、智慧交通运输、智慧路桥监测、智慧农业农村),理解低空经济创造的新质生产力,探讨低空经济推进智慧城乡建设和国家经济发展的逻辑与路径。

城镇密集区公路桥群绿色建造创新探索

为提升城镇密集区公路绿色建造水平,结合南(沙)—中(山)高速的项目特点及工程需求,将绿色发展理念贯彻到公路设计、施工、运营、养护和管理的全寿命周期,重点研发了公路桥群工业化智能建造、公路资源综合利用、滨海湿地区公路环境保护与粤港澳大湾区交旅融合设计等四类十项创新技术。其中,预制梁环形生产线、非标准八车道等为项目特色首创技术,初步构建了典型城市群绿色桥群公路技术体系,并对未来重点发展方向进行了展望,以期丰富和完善粤港澳大湾区绿色公路技术体系,为其他高度城镇化区域可持续发展提供参考。

数智赋能“一带一路”教育合作与课程建设

“一带一路”教育合作正在不断深化,数智技术赋能“一带一路”教育合作,推进数智化课程建设,应从支持机制、资源建设、平台融合、智能升级等多方面进行剖析。数智化课程要充分体现正能量,匹配学习者需求,符合在线课程特点,具备灵活的交互机制,避免产生资源堆砌、与线下课程脱节和智能化程度不高等问题。建设好线下教育实践基地、增强课程的体验感对于发挥课程育人效果也非常关键。

“一带一路”倡议背景下西部高校引智工作探索与实践

高校引进国外智力(简称“引智”)工作是提升学校科研创新能力的重要支撑。西部高校由于深处西北内陆,地域空间优势和经济发展能力远不如沿海城市,吸才引智工作举步维艰。“一带一路”倡议的实施为西部沿线城市带来国际合作的新机遇,该文在分析西部高校引智工作现状的基础上,着力探讨如何搭乘丝绸之路顺风车,突破引智局限,改善引智方法;建立“顶层规划、多元模式、稳才留才、提质增效”的创新型引智体系,为西部高校打开国际化新格局,更好地服务于“一带一路”倡议的实施。

基于多目标优化的变电站消防资源配置算法

针对变电站消防资源分配与分布不均匀的问题,提出了一种基于多目标优化的变电站消防资源配置算法。该算法采用电力生产场所的道路网作为变电站消防责任区划分的依据,并结合各划分区域的消防需求构建消防资源配置模型。根据该模型的特点,在帕累托最优解的约束下提出了一种基于模拟退火的多目标优化算法,来求解该消防资源配置模型。仿真与实验结果表明,所提出的消防资源配置模型更符合实际变电站各场所分布的特点,且所提出的多目标优化算法得到的消防资源分配结果能在消防资源有限的条件下,实现最大的配置效益和快速的消防响应。

路面结冰检测技术发展研究

开展路面结冰检测是预防和减少交通事故的重要手段。首先,从气候条件、环境、道路基础设施这三个方面对路面结冰的影响因素进行了介绍;其次,分析了路面结冰的常见检测技术,包括基于气象数据的预测方法、基于路面状况的检测方法、基于遥感技术应用、基于人工智能与大数据技术融合应用,并从技术原理、常见应用等方面进行检测技术的探讨,每项技术都有其优势和缺点,多技术结合可有效提升结冰检测的精度和可靠性。

智慧交通系统中关于道路交通事故处理方面的应用

随着城市交通压力增大,传统管理模式难适应需求,智慧交通系统应运而生。提出一种基于残差网络的智能轻微交通事故处理系统,能自动预处理事故现场图像,利用残差网络构建事故损伤鉴定模型,通过深度学习技术自动识别和评估车辆损伤程度。同时,利用大数据与AI技术进行迁移学习,提取并融合多车事故多角度图像特征,构建事故责任鉴定模型,为智能交通系统发展提供新思路。

智能传感器在沥青路面服役性态监测中的应用

为促进公路基础设施的智能化,实现道路铺装结构的智能感知功能,研究阐释了道路传感器的技术类型和实践应用,分析了沥青路面传感器应用的技术挑战,结果表明,智能传感器在沥青路面服役性态监测中发挥了至关重要的作用,根据实施的技术路径不同,将道路智能传感器分为非接触式、表面接触式和内埋式,可实现对路域环境、路表功能状态及内部结构响应等目标信息的连续监测;受限于沥青混合料路面恶劣的施工与工作环境等影响,传感器在沥青路面性态监测中尚面临感知不精准、协同工作机理不明晰、结构损伤行为不明确及感知功能难持久等技术问题。研究成果可为促进传感器技术在沥青路面智能监测方面的应用提供重要参考价值。