# Online Monitoring of Object Detection Performance During Deployment

arXiv: https://arxiv.org/abs/2011.07750 · HTML: https://mlsanity.com/paper/2011.07750/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection](https://mlsanity.com/paper/2208.13930/) (2022)
- [Hyperdimensional Feature Fusion for Out-Of-Distribution Detection](https://mlsanity.com/paper/2112.05341/) (2021)
- [Run-time Introspection of 2D Object Detection in Automated Driving Systems Using Learning Representations](https://mlsanity.com/paper/2403.01172/) (2024)
- [Robustness Enhancement of Object Detection in Advanced Driver Assistance Systems (ADAS)](https://mlsanity.com/paper/2105.01580/) (2021)
- [Instance-Aware Observer Network for Out-of-Distribution Object Segmentation](https://mlsanity.com/paper/2207.08782/) (2022)

## Abstract (arXiv)

During deployment, an object detector is expected to operate at a similar performance level reported on its testing dataset. However, when deployed onboard mobile robots that operate under varying and complex environmental conditions, the detector's performance can fluctuate and occasionally degrade severely without warning. Undetected, this can lead the robot to take unsafe and risky actions based on low-quality and unreliable object detections. We address this problem and introduce a cascaded neural network that monitors the performance of the object detector by predicting the quality of its mean average precision (mAP) on a sliding window of the input frames. The proposed cascaded network exploits the internal features from the deep neural network of the object detector. We evaluate our proposed approach using different combinations of autonomous driving datasets and object detectors.