Er, Taha YasinSelcuk, Seda2025-05-132025-09-182025-05-132025-09-18202497983503863949798350386400https://doi.org/10.1109/SST61991.2024.10755318https://hdl.handle.net/20.500.12416/13122Efficient and accurate detection of urban road anomalies such as potholes, manhole covers, and speed bumps is crucial for enhancing urban infrastructure and ensuring road safety. However, detecting these small-scale features using machine learning is significantly challenged by the high prevalence of negative data and the complex urban backgrounds in images. This study introduces an innovative approach utilizing a Dynamic Cropping System (DCS) in conjunction with the YOLOv8 convolutional neural network model to refine the detection of these road anomalies. The DCS method enhances detection accuracy by employing a YOLOv8- based model to identify a nd i solate r oad s urfaces w ithin i mages, t hereby minimizing irrelevant background information through targeted cropping.eninfo:eu-repo/semantics/closedAccessConvolutional Neural Networks (CNN)Image PreprocessingObject DetectionUrban Road AnomaliesYOLOConference Object10.1109/SST61991.2024.107553182-s2.0-85212859807