A Dataset for Provident Vehicle Detection at Night

TL;DR

This paper introduces a large open-source dataset for vehicle detection at night based on light reflections, aiming to improve computer vision's ability to detect vehicles in low-light conditions.

Contribution

It provides an extensive annotated dataset focusing on light reflections at night and benchmarks detection models, addressing a neglected area in computer vision.

Findings

State-of-the-art models achieved baseline detection performance.

The dataset reveals challenges in detecting light reflections as cues.

Encourages research into night-time vehicle detection using visual cues.

Abstract

In current object detection, algorithms require the object to be directly visible in order to be detected. As humans, however, we intuitively use visual cues caused by the respective object to already make assumptions about its appearance. In the context of driving, such cues can be shadows during the day and often light reflections at night. In this paper, we study the problem of how to map this intuitive human behavior to computer vision algorithms to detect oncoming vehicles at night just from the light reflections they cause by their headlights. For that, we present an extensive open-source dataset containing 59746 annotated grayscale images out of 346 different scenes in a rural environment at night. In these images, all oncoming vehicles, their corresponding light objects (e.g., headlamps), and their respective light reflections (e.g., light reflections on guardrails) are labeled. In this context, we discuss the characteristics of the dataset and the challenges in objectively describing visual cues such as light reflections. We provide different metrics for different ways to approach the task and report the results we achieved using state-of-the-art and custom object detection models as a first benchmark. With that, we want to bring attention to a new and so far neglected field in computer vision research, encourage more researchers to tackle the problem, and thereby further close the gap between human performance and computer vision systems.