Pothole Detection Using Image Enhancement GAN and Object Detection Network

Many datasets used to train artificial intelligence systems recognize potholes, such as the challenging sequences for autonomous driving (CCSAD) and Pacific Northwest road (PNW) datasets, do not produce satisfactory results. This is due fact that these present complex but realistic scenarios of pothole detection tasks than popularly achieve better results effectively represents task. In remote sensing, super-resolution generative adversarial networks (GAN), enhanced (ESRGAN), have been employed mitigate issues small-object detection, which has shown remarkable performance in detecting small objects from low-quality images. Inspired by this success we apply similar techniques with an ESRGAN network improve image quality surfaces, use different object same pipeline detect instances potholes The architecture propose consists two main components: a network. For network, employ both you only look once (YOLOv5) EfficientDet networks. Comprehensive experiments on show our method compared state-of-the-art methods detection.