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An Integrated Deep Learning and Fuzzy Logic System for Road Crack Severity Analysis, and Pedestrian Fall Risk Prediction | ||
| Iranian Journal of Fuzzy Systems | ||
| دوره 22، شماره 6، بهمن و اسفند 2025، صفحه 103-123 اصل مقاله (1.21 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2025.52137.9268 | ||
| نویسندگان | ||
| Priti Chakurkar1؛ Deepali Vora* 2 | ||
| 11. Symbiosis Institute of Technology PUNE, Symbiosis International (Deemed University), Pune, India 2 School of Computer Engineering, Dr.Vishwanath | ||
| 2Symbiosis Institute of Technology PUNE, Symbiosis International (Deemed University), Pune, India | ||
| چکیده | ||
| Cracks on pedestrian sidewalks and walkways pose a significant safety hazard, increasing the risk of trips, falls, and injuries, particularly for vulnerable groups such as the elderly and children. Traditional crack detection and severity assessment methods are usually manual, time-consuming, and subjective, especially in the Indian context, where road and sidewalk inspections are still conducted mainly through visual surveys due to cost and infrastructure constraints. This paper proposes an integrated framework of deep learning and fuzzy logic to analyze sidewalk crack severity and predict pedestrian fall risk automatically. A novel crack quantification method using edge detection and adaptive segmentation is proposed to measure crack width accurately. A fine-tuned deep learning model is employed for automating crack severity prediction, which achieved 95% accuracy and demonstrated robustness to noise, blur, and lighting variations. To estimate fall risk, a fuzzy inference system is developed considering four inputs: crack severity, road condition, weather, and pedestrian age, and a set of expert-defined fuzzy rules is applied to estimate risk levels. The outcomes show the effectiveness of the proposed FIS scheme, which achieved 95% accuracy and outperformed non-fuzzy baseline approaches. | ||
| کلیدواژهها | ||
| Road Crack Quantification؛ Crack Severity؛ Deep Learning؛ Pedestrian Falling Risk؛ Fuzzy Logic؛ Triangular Membership Function | ||
| مراجع | ||
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