使用YOLOv7与ESRGAN改进的坑洞检测
Improved Pothole Detection Using YOLOv7 and ESRGAN
AI总结：
本文提出结合SRGAN超分辨率与YOLOv7的坑洞检测方法，验证低质量图像经超分放大后检测速度与精度均优于基准。
摘要：
坑洞是常见的道路危害，会对车辆造成损坏并给驾驶员带来安全风险。卷积神经网络（CNNs）的引入在工业界被广泛用于基于深度学习方法的物体检测，并在硬件改进和软件实现方面取得了显著进展。本文提出了一种独特且更优的算法，通过超分辨率生成对抗网络（SRGANs）实现超分辨率（SR），以保证使用低分辨率摄像头或低分辨率图像和视频流进行自动坑洞检测的可行性。随后，我们使用You Only Look Once（YOLO）网络，即YOLOv7网络，在低质量和高质量行车记录仪图像上建立了坑洞检测性能基线。然后，我们展示并检验了在低质量图像上实施超分辨率放大后，相较于基准所获得的速度和精度提升。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
