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This research article presents a mobile sensing framework (MSF) that utilizes multi-modal sensors to detect and integrate urban road surface conditions, specifically focusing on bump features of road surfaces (BFRS). The proposed method combines data from various sensors, including GPS, accelerometers, and cameras, processed through a fine-tuned TimesFM model and a YOLO model for enhanced detection accuracy. The results demonstrate the effectiveness of this approach for smart city applications, addressing t

Author

Sadid Rahman

Language

EN