Improved Pothole Detection Using YOLOv7 and ESRGAN

TL;DR

This paper enhances pothole detection accuracy in low-resolution images by integrating ESRGAN-based super resolution with YOLOv7, demonstrating improved speed and precision in real-world dashcam scenarios.

Contribution

It introduces a novel combination of SRGAN and YOLOv7 for effective low-resolution pothole detection, improving upon existing methods.

Findings

Enhanced detection accuracy on low-quality images

Increased processing speed after upscaling

Demonstrated effectiveness in real-world dashcam footage

Abstract

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.