# OpenNet: Incremental Learning for Autonomous Driving Object Detection with Balanced Loss

arXiv: https://arxiv.org/abs/2311.14939 · HTML: https://mlsanity.com/paper/2311.14939/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 28 references that are in the map.
- First posted on arXiv 25 Nov 2023.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Gradient Episodic Memory for Continual Learning](https://mlsanity.com/paper/1706.08840/) (2017)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Efficient Lifelong Learning with A-GEM](https://mlsanity.com/paper/1812.00420/) (2018)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Large Scale Incremental Learning](https://mlsanity.com/paper/1905.13260/) (2019)
- [Meta-Learning with Implicit Gradients](https://mlsanity.com/paper/1909.04630/) (2019)
- [Towards Open World Object Detection](https://mlsanity.com/paper/2103.02603/) (2021)
- [Frustratingly Simple Few-Shot Object Detection](https://mlsanity.com/paper/2003.06957/) (2020)
- [CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving](https://mlsanity.com/paper/2203.07724/) (2022)
- [Towards Open-Set Object Detection and Discovery](https://mlsanity.com/paper/2204.05604/) (2022)
- [SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving](https://mlsanity.com/paper/2106.11118/) (2021)
- [Learning to Detect Every Thing in an Open World](https://mlsanity.com/paper/2112.01698/) (2021)
- [Object Counting and Instance Segmentation with Image-level Supervision](https://mlsanity.com/paper/1903.02494/) (2019)
- [Revisiting Open World Object Detection](https://mlsanity.com/paper/2201.00471/) (2022)

## Built on by (most cited first, 2 of 2)

- [RICO: Two Realistic Benchmarks and an In-Depth Analysis for Incremental Learning in Object Detection](https://mlsanity.com/paper/2508.13878/) (2025)
- [Incremental Object Detection with Prompt-based Methods](https://mlsanity.com/paper/2508.14599/) (2025)

## Abstract (arXiv)

Automated driving object detection has always been a challenging task in computer vision due to environmental uncertainties. These uncertainties include significant differences in object sizes and encountering the class unseen. It may result in poor performance when traditional object detection models are directly applied to automated driving detection. Because they usually presume fixed categories of common traffic participants, such as pedestrians and cars. Worsely, the huge class imbalance between common and novel classes further exacerbates performance degradation. To address the issues stated, we propose OpenNet to moderate the class imbalance with the Balanced Loss, which is based on Cross Entropy Loss. Besides, we adopt an inductive layer based on gradient reshaping to fast learn new classes with limited samples during incremental learning. To against catastrophic forgetting, we employ normalized feature distillation. By the way, we improve multi-scale detection robustness and unknown class recognition through FPN and energy-based detection, respectively. The Experimental results upon the CODA dataset show that the proposed method can obtain better performance than that of the existing methods.