Improved Pothole Detection Using YOLOv7 and ESRGAN
This addresses road safety by enhancing pothole detection with low-resolution cameras, but it is incremental as it combines existing methods.
The paper tackles pothole detection by using ESRGAN for super-resolution on low-quality images and YOLOv7 for detection, reporting improved speed and accuracy over a baseline.
Potholes are common road hazards that is causing damage to vehicles and posing a safety risk to drivers. The introduction of Convolutional Neural Networks (CNNs) is widely used in the industry for object detection based on Deep Learning methods and has achieved significant progress in hardware improvement and software implementations. In this paper, a unique better algorithm is proposed to warrant the use of low-resolution cameras or low-resolution images and video feed for automatic pothole detection using Super Resolution (SR) through Super Resolution Generative Adversarial Networks (SRGANs). Then we have proceeded to establish a baseline pothole detection performance on low quality and high quality dashcam images using a You Only Look Once (YOLO) network, namely the YOLOv7 network. We then have illustrated and examined the speed and accuracy gained above the benchmark after having upscaling implementation on the low quality images.