Pothole Detection for Safer Commutes with the help of Deep learning and IOT Device’s

Pothole Detection for Safer Commutes with the help of Deep learning and IOT Device’s

Narayan Dasadhikari1 , Abhishek Bagade2 , Tejas Gaikwad 3, Sojwal Magar

1Zeal College of Engineering and Research Narhe, Pune, India.

Abstract – Accidents caused by potholes have become a pressing concern in modern life. To address this problem effectively, a multi-step approach is proposed. The first crucial step involves the development of a dedicated device integrated into vehicles. This device is designed to continuously scan the road surface, identifying potholes inreal-time. When a pothole is detected, it promptly alerts the driver, allowing them to take evasive action to avoid the hazard. The second step entails the implementation of a technique that enables the device to determine the precise location of each detected pothole using the Global Positioning System (GPS). This GPS data can be collected and stored locally, either through a GPRS (General Packet Radio Service) module or a Bluetooth module. In the third aspect of the solution, the stored database is linked to a network system that incorporates mapping software, such as Google Maps or OpenStreetMap. This integration allows for a comprehensive representation of pothole locations and conditions, making the data accessible to authorities andother road users. Furthermore, to ensure data accessibility andrealtime updates, the system can transfer the database to the cloud using Wi-Fi or advanced 5G technology. This connectivity ensures that the information is readily available to all stakeholders, enhancing safety measures and enabling timely road maintenance. This project, "IoT Infused Movable Road Dividers for Enhanced Urban Mobility," aims to address the challenges of urban traffic congestion and road management by introducing innovative technology solutions. The project focuses on the integration of Internet of Things (IoT) technology with movable roaddividers to enhance urban mobility

1.INTRODUCTION

ThisRoadsareafundamentalcomponentofoursocietyand serveastheprimarymeansoftransportation.Overtime,our reliance on road networks has significantly increased, reflectingtheircrucialroleinourdailylives.However,the evolvingdemandsonroadinfrastructurecallforinnovative solutionstoaddressvariouschallenges.Thisprojectcenter’s on the integration of Internet of Things (IoT) technology withconventional,staticroaddividers,transformingthem into dynamic elements capable of real-time adjustments. LeveragingIoT, theseroad dividers become responsive to changingtrafficpatterns,events,andthedynamicnatureof urbanenvironments.Theprimarygoalistoenhancetraffic flowandalleviatecongestion,whicharepersistentissuesin urban mobility. In addition to traffic-related challenges, potholes pose a significant problem in road development

andmaintenance.Thisprojectacknowledgestheimportance ofsustainability,safety,andefficiencyinurbanmobility.It alignswiththebroaderscopeofsmartcityinitiativesaimed at utilizing technology for urban improvement. The introductionofasystemthatenhancestheadaptabilityand flexibilityofroadinfrastructureaimstoofferresidentsand visitors a more efficient, secure, and environmentally friendly commuting experience. This project seeks to contributetotheevolutionofurbanroadsystems,making themmoreresponsiveandadaptivetotheneedsofmodern urban center’s while addressing critical issues such as congestionandroadquality.Bymakingapotholedetecting vehicle system an establishing an inter-vehicular communication,itispossibletoreduce theaccidents. The project'ssignificanceliesinitspotentialtorevolutionizethe way road hazards are addressed, ultimately making commutessaferandmoreconvenientforeveryone.Inthis introduction, we embark on a journey to explore how IoT anddeeplearningcanbeharnessedtotransformtheurban commuting experience, reduce accidents, and ensure smoother,moresecurejourneysonourroadways.

2.LITERATURE SURVEY

1)MohanPrakash,SriharipriyaK.Cproposedatheoretical paper on the topic “Enhanced Pothole Detection System Using YOLOx Algorithm” which stated or gave the basic informationontheYOLOalgorithmanditsversionandthere performance basedonthenumberofepochwithmodelare trainedandalsogetsthedetailed execution time need for each version of yolo algorithm the result it gets that the YOLOxgivehighprecisionvaluethanothermodelswiththe minimum number of epochs YOLOX-nano model outperforms all other lightweight detectors by a large marginandperformslesswhencomparedwithheavyweight modelsonlybyasmallmargin whichwaspublishedaround May 2022 which threw light on the machine learning algorithms.

2) Rupsha Debnath, Sayandeep Dutta, Soumajit Karmakar researchedonhowfasttheYOLOalgorithmworksandhow isthefunctioningoftheYOLOalgorithmontheirpaper“Fast PotholeDetectionWithTheYOLOAlgorithm”introducethe innovative system for pothole detection with the configurationofraspberrypigsmmodule,camersmodule fordetentionandwiththehelpofyoloalgorithmitgetthe highprecisionandaccuracy.the publishedaroundJanuary 2022inordertodealwithincreasingnumberofpotholesin WestBengal.

3) Shambhu Hegde, Harish V. Mekali, Golla Varaprasad researchedonhowaprototypewhichwasmadebythemcan fitintovehiclessothattheinter-vehicularcommunication should come out at ease in their paper “Pothole Deection AndInterVehicularCommunication”publishedinyear2014 IEEEpaper

4) Geethapriya.S, N. Duraimurugan, S.P. Chokkalingam proposedatheoryresearchpaperonthetopic“Real-Time Object Detection With YOLO ” published on International JournalofEngineeringandAdvancedTechnology(IJEAT)in the year February 2019 with a view of detecting a onject usingYOLOalgorithm

3.METHODOLOGY

1) ROBOFLOW :-

Roboflow is a comprehensive platform designed to streamline the process of managing, preparing, and using image datasets for machine learning and computer vision projects.Itoffersarangeoftoolsandfeaturestofacilitate dataset management and annotation, making it easier for developersanddatascientiststoworkwithimagedata.

Dataset Management: Roboflow provides a user-friendly interface for uploading, organizing, and managing image datasets.Itsupportsavarietyofdatasetformatsandallows userstocustomizehowdataisstructuredandorganized.

Data Augmentation: The platform offers built-in data augmentationtoolstoincreasethediversityofyourdataset. This can help improve the model's generalization and performance.LabellingandAnnotation:Roboflow simplifies the process of labelling and annotating images for object detection and other computer vision tasks. It supports variousannotationformatsandallowsforefficientlabelling of objects with bounding boxes, polygons, and more. Data Pre-processing: Users can perform data pre-processing tasks, such as resizing, normalization, and format conversion,withintheplatformtopreparedataformachine learningmodels.

Data Versioning: Roboflow offers version control for datasets, allowing you to track changes and updates over time. This is particularly useful when collaborating with teammembers.

Exporting Datasets: The platform allows users to export datasets in various formats, including YOLO, COCO, and TensorFlow, making it compatible with a wide range of machinelearningframeworks.

2)YOLO V8

YOLOisa popularobjectdetection algorithm incomputer vision. It revolutionized object detection by introducing a real-time, single-pass approach to identifying objects in imagesandvideos.

The name"You OnlyLook Once" comes fromthe factthat YOLO processes the entire image in a single forward pass through the neural network, as opposed to previous methodswhichofteninvolvedmultiplepassesorregionsof interest.ThismakesYOLOveryefficientandcapableofrealtimedetection

DividingtheImage:YOLOtakesaninputimageanddividesit intoagridofcells.Eachcellisresponsibleforpredictingthe presenceandlocationofobjects.

ConfidenceScore:Thealgorithmassignsaconfidencescore toeachboundingbox,indicatingtheprobabilitythatthebox contains an object. Bounding boxes with low confidence scoresarefilteredouttoimproveaccuracy.

Final Output: The final output of the YOLO algorithm includes a list of bounding boxes, each associated with a class label and a confidence score. This output provides informationabouttheobjectsdetectedintheimage.

Real-TimeProcessing:YOLO'sstrengthliesinitsabilityto performobjectdetectioninreal-time,makingitsuitablefor applications like autonomous driving, surveillance, and videoanalysis.

Training:YOLOistrainedonlabeleddatasets,whereeach trainingimageincludesboundingboxesandclasslabelsfor theobjectsofinterest.Duringtraining,themodellearnsto makepredictionsforobjectlocationsandclasses.

Sample paragraph Define abbreviations and acronyms the firsttimetheyareusedinthetext,evenaftertheyhavebeen definedintheabstract.AbbreviationssuchasIEEE,SI,MKS,

Fig -1:DataAnnotationusingRoboflow

CGS,sc, dc,and rms do nothave to be defined. Do not use abbreviations in the title or heads unless they are unavoidable

3 Components

1) ArduinoUNO:

ThesystemusesArduinoUnothatcanbeemployedtocreate a sensor system. This system include sensors like accelerometersorultrasonicsensorstodetectirregularities intheroadsurface,indicatingthepresenceofapothole.The Arduino Uno processes the sensor data and can trigger actionslikealertingadriverorloggingthepotholelocation formaintenancepurposes.Thissolutioncanhelpimprove roadsafetyandmaintenanceefforts.

2) OV7670 Camera:

WeusedanOV7670camera moduleforpotholedetection with an Arduino Uno that can be a rewarding project. We connectedtheOV7670cameramoduletotheArduinoUno. The OV7670typicallycommunicatesusingthe SCCB(I2Clike) protocol. We also connected the necessary pins betweenthecameramoduleandtheArduinoUno.Referto thedatasheetordocumentationforpindetails.

3) GlobalPositioningSystem(SIM900GPRSmodule):

WeusedtheSIM900GSM/GPRSmodulethatcanbeusedin thepotholedetectionmodelforremotecommunicationand storingthelocationofa potholeina databasesystemand alertingthesystemofgovernmentagencies.Itenablesthe devicetosenddataoralertsviaGSMnetwork.

4. Future Scope

TheintegrationofIoTdevices,equippedwithcamerasand sensors, can be expanded to capture multimodal data, providingarichersetofinformationfortheYOLOalgorithm to analyze. This could involve incorporating additional sensors, such as accelerometers or depth sensors, to improve the algorithm's ability to precisely locate and classifypotholes.

Real-time data integration and communication between YOLO-enableddevicesandcentralizedtrafficmanagement systems could be further developed. This could enable instant alerts and responses to detected potholes, contributing to more proactive and efficient road maintenance. Moreover, the collaboration of YOLO-based potholedetectionsystemswithautonomousvehiclescanbe explored, potentially enhancing navigation and safety for self-drivingvehicles.

In terms of scalability, future research may focus on optimizing YOLO fordeploymentonresource-constrained IoTdevices.Thisincludesconsiderationsforenergy-efficient implementations to prolong device lifespan and reduce maintenance requirements, making the system more sustainableforlarge-scaledeployment.

Fig -2:YOLOV8

Fig -1:ArduinoUNO

AsYOLOisknownforitsspeedandefficiency,thealgorithm couldplayacrucialroleinreal-timepredictivemaintenance models. By analyzing historical data,YOLO-basedsystems can contribute to forecasting potential pothole formation, allowing for proactive and strategic infrastructure maintenances.

REFERENCES

[1] Wang,Hsiu-Wen&Chen,Chi-Hua&Cheng,Ding-Yuan& Lin, Chun-Hao & Lo, Chi-Chun. (2015). A Real-Time Pothole Detection Approach for Intelligent Transportation System. Mathematical Problems in Engineering.2015.1-7.10.1155/2015/869627.

[2] Dalmia, Dev & Jain, Reetu & Hussain, Syed. (2023). Intelligent System for Real-Time Potholes Monitoring andDetection.10.1007/978-981-99-1431-9_32.

[3] Wyawahare, Medha & Chaure, Nayan & Bhosale, Dhairyashil & Phadtare, Ayush. (2023). Comparative Analysis and Evaluation of Pothole Detection Algorithms.10.1007/978-981-99-5166-6_62.

[4] Reddy, Greeshma & V, Brunda & S, Bhoomika & P, Bhavana & S, Suma. (2023). Pothole Detection Using Deep Learning. International Journal of Innovative Research in Advanced Engineering. 10. 207-210. 10.26562/ijirae.2023.v1005.12.

[5] B,Nataraju&J,Akarsh&Kumar,Lakkam&Sanjay,H& V, Harshit. (2023). Automatic Pothole and Humps Detection System of Roads. 1. 16-20. 10.48001/jofsn.2023.1116-20.

[6] Haimer, Zineb & Khalid, Mateur & Farhan, Youssef & Madi, Abdessalam. (2023). Pothole Detection: A Performance Comparison Between YOLOv7 and YOLOv8.1-7.10.1109/ICOA58279.2023.10308849.

[7] Bucko,Boris&Lieskovska,Eva&Zábovská,Katarína& Zábovský, Michal. (2022). Computer Vision Based Pothole Detection under Challenging Conditions. Sensors.22.8878.10.3390/s22228878.

[8] Anandhalli, Mallikarjun & Tanuja, A. & Baligar, Vishwanath & Baligar, Pavana. (2022). Indian pothole detectionbasedonCNNandanchor-baseddeeplearning method. International Journal of Information Technology.14.10.1007/s41870-022-00881-5.

[9] Gupta,Saksham&Sharma,Paras&Sharma,Dakshraj& Gupta,Varun&Sambyal,Nitigya.(2020).Detectionand localization of potholes in thermal images using deep neuralnetworks.MultimediaToolsandApplications.79. 10.1007/s11042-020-09293-8.

[10] Wang,Hsiu-Wen&Chen,Chi-Hua&Cheng,Ding-Yuan& Lin, Chun-Hao & Lo, Chi-Chun. (2015). A Real-Time Pothole Detection Approach for Intelligent Transportation System. Mathematical Problems in Engineering.2015.1-7.10.1155/2015/869627.

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