# IMMVP: An Efficient Daytime and Nighttime On-Road Object Detector

arXiv: https://arxiv.org/abs/1910.06573 · HTML: https://mlsanity.com/paper/1910.06573/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2019.

## Built on by (most cited first, 4 of 4)

- [ContextVLM: Zero-Shot and Few-Shot Context Understanding for Autonomous Driving using Vision Language Models](https://mlsanity.com/paper/2409.00301/) (2024)
- [Using simulation to quantify the performance of automotive perception systems](https://mlsanity.com/paper/2303.00983/) (2023)
- [ICON2: Reliably Benchmarking Predictive Inequity in Object Detection](https://mlsanity.com/paper/2306.04482/) (2023)
- [ISETHDR: A Physics-based Synthetic Radiance Dataset for High Dynamic Range Driving Scenes](https://mlsanity.com/paper/2408.12048/) (2024)

## Abstract (arXiv)

It is hard to detect on-road objects under various lighting conditions. To improve the quality of the classifier, three techniques are used. We define subclasses to separate daytime and nighttime samples. Then we skip similar samples in the training set to prevent overfitting. With the help of the outside training samples, the detection accuracy is also improved. To detect objects in an edge device, Nvidia Jetson TX2 platform, we exert the lightweight model ResNet-18 FPN as the backbone feature extractor. The FPN (Feature Pyramid Network) generates good features for detecting objects over various scales. With Cascade R-CNN technique, the bounding boxes are iteratively refined for better results.