AI-POWERED RAIL TRACK AND ROAD POTHOLE FAULT DETECTION SYSTEM USING ADVANCED DEEP LEARNING TECHNIQUES FOR ENHANCED INFRASTRUCTURE SAFETY

Yakubu Zarahdeen; Okere Chidiebere Emmanuel

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Submission Last date	28-Jun-2025
Acceptance Status	In One Day
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Title: :  AI-POWERED RAIL TRACK AND ROAD POTHOLE FAULT DETECTION SYSTEM USING ADVANCED DEEP LEARNING TECHNIQUES FOR ENHANCED INFRASTRUCTURE SAFETY
PaperId: :  26102
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 11 Issue 3 2025
DUI:    16.0415/IJARIIE-26102
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Name	Author Institute
Yakubu Zarahdeen	Department of Computing Technology, SRM Institute of Science and Technology, India
Okere Chidiebere Emmanuel	Fedpoly Kaltungo

Abstract

Research Area	Science and Tech
KeyWord	Infrastructure monitoring, Deep learning, Fault detection, SMOTE, Convolutional Neural Networks, Rail track inspection, Pothole detection
Abstract	This study presents an AI-powered fault detection system for rail tracks and road potholes using a Modified Deep Convolutional Neural Network (DCNN) with Synthetic Minority Over-sampling Technique (SMOTE). The proposed model addresses critical challenges in infrastructure monitoring: detecting subtle defects and handling class imbalance in imbalanced datasets. By incorporating residual connections and attention mechanisms, the DCNN achieves superior feature extraction, while SMOTE significantly improves detection of minority fault classes. Experimental results demonstrate exceptional performance, with 98.2% accuracy for rail track faults and 97.5% for road potholes. The system achieves 96.5% recall for rail defects and 95.8% for potholes - a 15% improvement over baseline methods. With real-time processing at 45ms per image, the solution is deployable on edge devices for continuous monitoring. Key innovations include: (1) a novel DCNN architecture optimized for infrastructure defects, (2) effective SMOTE integration for class imbalance mitigation, and (3) comprehensive validation on diverse datasets. The system's high precision (97.8% for rails, 96.3% for roads) minimizes false alarms, while its recall ensures critical faults are rarely missed. This research contributes to safer, more efficient infrastructure maintenance by providing: (1) a robust AI framework for defect detection, (2) practical solutions for real-world deployment challenges, and (3) benchmarks for future work in smart infrastructure monitoring. The results highlight the potential of deep learning to transform traditional inspection paradigms, reducing costs while improving reliability.

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IJARIIE	Yakubu Zarahdeen, and Okere Chidiebere Emmanuel. "AI-POWERED RAIL TRACK AND ROAD POTHOLE FAULT DETECTION SYSTEM USING ADVANCED DEEP LEARNING TECHNIQUES FOR ENHANCED INFRASTRUCTURE SAFETY" International Journal Of Advance Research And Innovative Ideas In Education Volume 11 Issue 3 2025 Page 375-390
MLA	Yakubu Zarahdeen, and Okere Chidiebere Emmanuel. "AI-POWERED RAIL TRACK AND ROAD POTHOLE FAULT DETECTION SYSTEM USING ADVANCED DEEP LEARNING TECHNIQUES FOR ENHANCED INFRASTRUCTURE SAFETY." International Journal Of Advance Research And Innovative Ideas In Education 11.3(2025) : 375-390.
APA	Yakubu Zarahdeen, & Okere Chidiebere Emmanuel. (2025). AI-POWERED RAIL TRACK AND ROAD POTHOLE FAULT DETECTION SYSTEM USING ADVANCED DEEP LEARNING TECHNIQUES FOR ENHANCED INFRASTRUCTURE SAFETY. International Journal Of Advance Research And Innovative Ideas In Education, 11(3), 375-390.
Chicago	Yakubu Zarahdeen, and Okere Chidiebere Emmanuel. "AI-POWERED RAIL TRACK AND ROAD POTHOLE FAULT DETECTION SYSTEM USING ADVANCED DEEP LEARNING TECHNIQUES FOR ENHANCED INFRASTRUCTURE SAFETY." International Journal Of Advance Research And Innovative Ideas In Education 11, no. 3 (2025) : 375-390.
Oxford	Yakubu Zarahdeen, and Okere Chidiebere Emmanuel. 'AI-POWERED RAIL TRACK AND ROAD POTHOLE FAULT DETECTION SYSTEM USING ADVANCED DEEP LEARNING TECHNIQUES FOR ENHANCED INFRASTRUCTURE SAFETY', International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 3, 2025, p. 375-390. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/AI_POWERED_RAIL_TRACK_AND_ROAD_POTHOLE_FAULT_DETECTION_SYSTEM_USING_ADVANCED_DEEP_LEARNING_TECHNIQUES_FOR_ENHANCED_INFRASTRUCTURE_SAFETY_ijariie26102.pdf (Accessed : ).
Harvard	Yakubu Zarahdeen, and Okere Chidiebere Emmanuel. (2025) 'AI-POWERED RAIL TRACK AND ROAD POTHOLE FAULT DETECTION SYSTEM USING ADVANCED DEEP LEARNING TECHNIQUES FOR ENHANCED INFRASTRUCTURE SAFETY', International Journal Of Advance Research And Innovative Ideas In Education, 11(3), pp. 375-390IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/AI_POWERED_RAIL_TRACK_AND_ROAD_POTHOLE_FAULT_DETECTION_SYSTEM_USING_ADVANCED_DEEP_LEARNING_TECHNIQUES_FOR_ENHANCED_INFRASTRUCTURE_SAFETY_ijariie26102.pdf (Accessed : )
IEEE	Yakubu Zarahdeen, and Okere Chidiebere Emmanuel, "AI-POWERED RAIL TRACK AND ROAD POTHOLE FAULT DETECTION SYSTEM USING ADVANCED DEEP LEARNING TECHNIQUES FOR ENHANCED INFRASTRUCTURE SAFETY," International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 3, pp. 375-390, May-Jun 2025. [Online]. Available: https://ijariie.com/AdminUploadPdf/AI_POWERED_RAIL_TRACK_AND_ROAD_POTHOLE_FAULT_DETECTION_SYSTEM_USING_ADVANCED_DEEP_LEARNING_TECHNIQUES_FOR_ENHANCED_INFRASTRUCTURE_SAFETY_ijariie26102.pdf [Accessed : ].
Turabian	Yakubu Zarahdeen, and Okere Chidiebere Emmanuel. "AI-POWERED RAIL TRACK AND ROAD POTHOLE FAULT DETECTION SYSTEM USING ADVANCED DEEP LEARNING TECHNIQUES FOR ENHANCED INFRASTRUCTURE SAFETY." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 11 number 3 ().
Vancouver	Yakubu Zarahdeen, and Okere Chidiebere Emmanuel. AI-POWERED RAIL TRACK AND ROAD POTHOLE FAULT DETECTION SYSTEM USING ADVANCED DEEP LEARNING TECHNIQUES FOR ENHANCED INFRASTRUCTURE SAFETY. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2025 [Cited : ]; 11(3) : 375-390. Available from: https://ijariie.com/AdminUploadPdf/AI_POWERED_RAIL_TRACK_AND_ROAD_POTHOLE_FAULT_DETECTION_SYSTEM_USING_ADVANCED_DEEP_LEARNING_TECHNIQUES_FOR_ENHANCED_INFRASTRUCTURE_SAFETY_ijariie26102.pdf

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