Stereo LWIR-RGB Automatic Labeling for Low-Light Camera-Based VRU Detection

Key points

• VRU detection in low-light conditions is a key component in ensuring VRU safety during the most collision prone time of the day.1
• Most autolabeling methods use RGB detections as their labels source, which cannot be replicated in low-light conditions.1
• We use LWIR-RGB image pairs and source our labels from LWIR as VRUs differentiate from the background due to their body temperature.1
• Models trained on our generated labels beat both off-the-shelf baselines and models fine tuned on a hand labeled subset on F1, mAP@50 and LAMR metrics when tested on a manually labeled dataset, and the result holds for two different LWIR detectors.1

Sources (1)

• [1]Stereo LWIR-RGB Automatic Labeling for Low-Light Camera-Based VRU Detection
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 03:31 PM

  To fill this gap in this paper we present an automated pipeline for low-light VRU labeling.

  VRU detection in low-light conditions is a key component in ensuring VRU safety during the most collision prone time of the day.

Extractive summary: sentences quoted from the sources.