Improved Pothole Detection Using YOLOv7 and ESRGAN
Nirmal Kumar Rout Gyanateet Dutta Varun Sinha Arghadeep Dey Subhrangshu Mukherjee Gopal Gupta
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.