Abstract
DeepLab V3+ semantic segmentation develops road segmentation from UAV images. First, a camera-equipped UAV captures road images from 3 altitudes in Perlis. The images will be resized and augmented to provide additional road images for deep learning model training. Next, images are manually segmented into road and background using CVAT. The DeepLab V3+ with Resnet-18, Resnet-50, and MobileNet V2 backbone network is utilised to segment the road using Matlab. Finally, the suggested method's performance is compared to all backbone network approaches at 3 various altitudes to determine pixel accuracy (PA), mean intersection over union (mIoU), and meanF1-score (meanF1). The study develops an accurate and robust approach for road segmentation from UAV images that road surveyors may employ for inspection and monitoring. This technique might be implemented to identify road cracks and potholes in the future study.
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