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- W4382118684 abstract "In advancing nations like India, fixing potholes on streets may be a critical responsibility, as accidents can occur owing to their presence. In this way, it is straightforward to distinguish them to guarantee the security of individuals. Many work inquiries have been conducted to date in order to efficiently identify the potholes, but the majority are economically not feasible. In current history, using deep learning algorithms to detect potholes became more popular. Convolutional neural network (CNN) algorithm became extremely good at identifying things. A 665 imaged dataset is created with labels. With the use of data augmentation techniques such as blurring, rotating, and flipping, the 665 image dataset was transformed into a dataset containing 1995 images. The images are then utilized for training using the Faster-RCNN algorithm with different feature extractors and compared with YOLO v5 of scales small and medium. Faster-RCNN and YOLO v5 architectures are used in object detection algorithms, there will always be a trade-off between latency and accuracy. On the basis of mAP (mean Average Precision), the final results are compared. YOLOv5 had an accuracy of 82% and a Recall of 71.5%, whereas Faster-RCNN had a precision of 63.9% and a Recall of 75%." @default.
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- W4382118684 date "2023-01-01" @default.
- W4382118684 modified "2023-09-26" @default.
- W4382118684 title "Pothole Detection Approach Based on Deep Learning Algorithms" @default.
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- W4382118684 doi "https://doi.org/10.1007/978-981-19-8669-7_53" @default.
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