An overcome of far-distance limitation on tunnel CCTV-based accident detection in AI deep-learning frameworks

TL;DR

This paper proposes a method to improve far-distance vehicle detection in tunnel CCTV systems by using inverse perspective transform and training deep learning models on warped images, enhancing detection accuracy for distant vehicles.

Contribution

It introduces a novel approach combining inverse perspective transform with deep learning to overcome tunnel CCTV distance limitations.

Findings

Warped image training improves detection accuracy for distant vehicles.

Deep learning models trained on warped images outperform those trained on original images.

The method effectively extends the effective range of tunnel CCTV accident detection.

Abstract

Tunnel CCTVs are installed to low height and long-distance interval. However, because of the limitation of installation height, severe perspective effect in distance occurs, and it is almost impossible to detect vehicles in far distance from the CCTV in the existing tunnel CCTV-based accident detection system (Pflugfelder 2005). To overcome the limitation, a vehicle object is detected through an object detection algorithm based on an inverse perspective transform by re-setting the region of interest (ROI). It can detect vehicles that are far away from the CCTV. To verify this process, this paper creates each dataset consisting of images and bounding boxes based on the original and warped images of the CCTV at the same time, and then compares performance of the deep learning object detection models trained with the two datasets. As a result, the model that trained the warped image was able to detect vehicle objects more accurately at the position far from the CCTV compared to the model that trained the original image.