Deep Learning Method to Detect the Road Cracks and Potholes for Smart Cities

The increasing global population at a rapid pace makes road trafficdense;managing such massive traffic is challenging. In developing countrieslike Pakistan, road traffic accidents (RTA) have the highest mortality percentageamong other Asian countries. The main reasons for RTAs are roadcracks and potholes. Understanding the need for an automated system forthe detection of cracks and potholes, this study proposes a decision supportsystem (DSS) for an autonomous road information system for smart citydevelopment with the use of deep learning. The proposed DSS works in layerswhere initially the image of roads is captured and coordinates attached to theimage with the help of global positioning system (GPS), communicated tothe decision layer to find about the cracks and potholes in the roads, andeventually, that information is passed to the road management informationsystem, which gives information to drivers and the maintenance department.For the decision layer, we projected a CNN-based model for pothole crackdetection (PCD). Aimed at training, a K-fold cross-validation strategy wasused where the value of K was set to 10. The training of PCD was completedwith a self-collected dataset consisting of 6000 images from Pakistani roads.The proposed PCD achieved 98% of precision, 97% recall, and accuracy whiletesting on unseen images. The results produced by our model are higher thanthe existing model in terms of performance and computational cost, whichproves its significance.

The relationship between diameter and depth of potholes eroded by running water

Geometrical analyses of 3930 potholes （3565 fluvial potholes, 237 marine potholes and 128 hillside potholes） from 33 localities in the world reveal a consistent, linear relationship： D Nh ＋ M, where h and D are, respectively, the depth and mean diameter of pothole, M is a critical size of the initial concavities （seminal potholes） that subsequently underwent growth, and N is the ratio of diameter expanding （wall erosion） speed to deepening （floor abrasion） speed. For the stream potholes, N is generally less than 1 with an average value of 0.67, M varies from 5.3 cm to 40.5 cm with an average of 20 cm, and N decreases gently with increasing M. However, the marine and hillside potholes are generally characterized by N 〉 1 and M 〈 10-14 cm, and a power-law relationship N 4.24M o.78 （coefficient of determination R2 0.75, M is in cm） exists. The results indicate that depth increases faster than diameter for stream potholes due to the larger size of grinding stones （〉5-10 cm）, while depth increases slower than diameter for marine potholes and hillside potholes due to the smaller size of grinding stones （〈5-10 cm）. The pothole h-D relationship is nearly independent of rock type. Knowledge of the pothole depth-diameter relationship is useful in a number of contexts, including simulation of hydraulic dynamics, theoretical considerations of erosion, comprehension of channel incision and development of canyons and gorges, and accurate estimation of excavation volume and mechanical strength ofpotholed bedrock in the design and stability analysis of hydraulic and environmental engineering projects （e.g. dam construction and river dredging）.

Convergence of Stereo Vision-Based Multimodal YOLOs for FasterDetection of Potholes

Road potholes can cause serious social issues,such as unexpected damages to vehicles and traffic accidents.For efficient road management,technologies that quickly find potholes are required,and thus researches on such technologies have been conducted actively.The three-dimensional(3D)reconstruction method has relatively high accuracy and can be used in practice but it has limited application owing to its long data processing time and high sensor maintenance cost.The two-dimensional(2D)vision method has the advantage of inexpensive and easy application of sensor.Recently,although the 2D vision method using the convolutional neural network(CNN)has shown improved pothole detection performance and adaptability,large amount of data is required to sufficiently train the CNN.Therefore,we propose a method to improve the learning performance of CNN-based object detection model by artificially generating synthetic data similar to a pothole and enhancing the learning data.Additionally,to make the defective areas appear more contrasting,the transformed disparity map(TDM)was calculated using stereo-vision cameras,and the detection performance of the model was further improved through the late fusion with RGB(Red,Green,Blue)images.Consequently,through the convergence of multimodal You Only Look Once(YOLO)frameworks trained by RGB images and TDMs respectively,the detection performance was enhanced by 10.7%compared with that when using only RGB.Further,the superiority of the proposed method was confirmed by showing that the data processing speed was two times faster than the existing 3D reconstruction method.

Formation of Potholes Associated with Bedrock Gorges on Mesozoic Sandstone of Khari River, Kachchh Mainland, Western India

The potholes are perceptible erosional features associated with bedrock channels. They play an essential role in bedrock incision studies, but little work has been published on the development of potholes, especially in Bedrock Rivers in India. The present site-specific study aims to analyze the role of physical properties of bedrock and associated structures in the process of development of potholes and to classify the potholes based on their dimensions. The Khari River gorge formed over Mesozoic Sandstone with six bedrock terraces exposed along a confined segment of the river. The site is manifested by strath terraces, grooves, potholes, and knick points. Three stretches of Khari Gorge have been studied in detail for this purpose. The pothole dimensions, joint orientations, rock mass strength, physical properties of rock, and placing of potholes to present active channels were measured. The analysis shows the inverse relationship between the size of pothole and rock properties. The distance of potholes and active channels govern the essential factor and manifest the role of flow hydraulics. The high density of matured potholes near active channels over immature potholes supports primary control of hydraulics of flow over rock properties.

Computer vision for road imaging and pothole detection:a state-of-the-art review of systems and algorithms

Computer vision algorithms have been utilized for 3-D road imaging and pothole detection for over two decades.Nonetheless,there is a lack of systematic survey articles on state-of-the-art(SoTA)computer vision techniques,especially deep learningmodels,developed to tackle these problems.This article first introduces the sensing systems employed for 2-D and 3-D road data acquisition,including camera(s),laser scanners and Microsoft Kinect.It then comprehensively reviews the SoTA computer vision algorithms,including(1)classical 2-D image processing,(2)3-D point cloud modelling and segmentation and(3)machine/deep learning,developed for road pothole detection.The article also discusses the existing challenges and future development trends of computer vision-based road pothole detection approaches:classical 2-D image processing-based and 3-D point cloud modelling and segmentation-based approaches have already become history;and convolutional neural networks(CNNs)have demonstrated compelling road pothole detection results and are promising to break the bottleneck with future advances in self/un-supervised learning for multi-modal semantic segmentation.We believe that this survey can serve as practical guidance for developing the next-generation road condition assessment systems.

Comparison of yield traits in rice among three mechanized planting methods in a rice-wheat rotation system

Understanding the differences in yield traits of rice among pothole seedling of mechanical transplanting （PSMT）, carpet seedling of mechanical transplanting （CSMT） and mechanical direct seeding （MDS） is of great importance not only for rice scientists but also for rice farmers to develop a high-yield production system under mechanical conditions in a rice-wheat rotation system. However, such traits are yet to be studied among rice varieties ofjaponica-indica hybrid rice （JIHR）,japonica conventional rice （JCR） and indica hybrid rice （IHR）. Field experiments were conducted in 2014 and 2015, where six cultivars of the three rice types JIHR, JCR and IHR were grown individually with PSMT, CSMT and MDS methods, under respective managements for each method to achieve the maximum attainable yield. Results showed that （i） the PSMT significantly increased grain yield of JIHR by 22.0 and 7.1%, of JCR by 15.6 and 3.7% and of I HR by 22.5 and 7.4%, compared to MDS and CSMT on average across the two years, respectively. The highest yield was produced by the combination of JIHR and PSMT; （ii） high yield under PSMT was mainly attributed to large sink capacity and high-efficient dry matter accumulation. With sufficient panicles per hectare, the increase of spikelet number per panicle, especially the increase in spikelet number of the secondary rachis-branches was determined to be the optimal approach for developing a large sink capacity for rice under PSMT. The optimal tillers development, large leaf area index at heading stage, and high leaf area duration, crop growth rate and net assimilation rate during grain-filling phase could be the cause of sufficient dry matter accumulation for rice under PSMT; （iii） moreover, the PSMT favored plant growth as well as enriched the stems plus sheaths during grain-filling phase, as compared with CSMT and MDS. These results suggest that PSMT may be an alternative approach to increasing grain yield in a rice-wheat rotation system in the lower reaches of the Yangtze River in China.

Development of a Virtual Environment for Simulation of a 3D Road Profile Using OpenCRG and MATLAB GUI

In vehicle dynamics, there are wide applications concerning the simulation of vehicles on roads. These simulation applications relate to vehicle driving, ride comfort and durability. An accurate prediction of simulation results requires reliability and efficiency of road representations. The MATLAB graphical user interface module, called MATLAB GUI, is used to develop virtual simulation laboratories that allow the user to interact with a computer program using graphical objects. In this context, the aim of this article is to use the MATLAB OpenCRG suite of tools and the MATLAB GUI to develop a virtual environment for simulating a 3D road profile. A three-dimensional model of a pothole with variable parameters is developed and integrated into the 3D road profile.

Simulation of rice paddy systems in SWAT:A review of previous applications and proposed SWAT+rice paddy module

The Soil and Water Assessment Tool(SWAT)is an ecohydrological watershed-scale model which was initially developed in the early 1990s to simulate the impacts of land use,management systems,and climate on hydrology and/or water quality.First adopted in the U.S.,the use of the model then spread to Europe and then later to Asia and other regions.The range of applications that SWAT has been applied to have also expanded dramatically,which influenced ongoing model development which has been virtually continuous over the past two decades.A key component of many SWAT applications in Asia is accounting for rice paddy production that is common in some subregions within the continent.However,most of these studies do not provide explicit details of how rice production was simulated in SWAT.Other research has revealed that significant problems occur when trying to represent rice paddy systems in standard versions of SWAT,due to limitations in algorithms based on the runoff curve number approach or the pothole option.In response,key modifications have been made to SWAT in recent studies that have resulted in more accurate representation of rice paddy systems.These developments point to the need for the incorporation of an enhanced rice paddy module within SWAT to better capture rice paddy hydrological and pollutant dynamics,which would support improved use of the model in Asia and other rice production regions.Subtopics related to simulating rice production in SWAT are discussed as follows:1)an overview of global rice production;2)history of SWAT development;3)typical approaches for simulating rice production;4)problems associated with the typical approaches;5)recent code modifications to address deficiencies in replicating rice paddy systems;6)recommendations for developing a standard rice paddy module for future SWAT codes.