Online Monitoring of Object Detection Performance During Deployment (2011.07750v2)

Abstract: 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.

• The paper proposes a cascaded neural architecture that predicts the mAP of object detectors in real time without needing ground-truth data.
• It leverages a sliding window approach to stabilize performance assessments and reduce false alarms from per-frame fluctuations.
• Experimental results on datasets like KITTI and Waymo show higher true positive rates than baseline methods at critical mAP thresholds.

Overview of "Online Monitoring of Object Detection Performance During Deployment"

The paper "Online Monitoring of Object Detection Performance During Deployment" by Quazi Marufur Rahman, Niko Sunderhauf, and Feras Dayoub addresses a significant problem in autonomous systems: the variability of object detection performance in real-world deployment scenarios. This variability poses a challenge as conventional object detection models are typically trained and validated on static datasets that might not represent all potential deployment environments.

Contribution and Methodology

The main contribution of the paper is a cascaded neural network architecture designed to predict the mean average precision (mAP) of an object detector in real-time during deployment. By leveraging internal features from the deep neural networks used in object detectors, the proposed system can assess performance without access to ground-truth data. This self-assessment capability is crucial for autonomous systems, such as self-driving cars, where safety and reliability are paramount.

The proposed system performs per-window assessments of object detection performance using a sliding window of frames. This technique mitigates false alarms typically triggered by per-frame evaluations, which can fluctuate substantially due to minor variations in the input, such as re-scaling or translation of images. By utilizing a sliding window approach, the system provides a more stable performance monitoring solution.

Experimental Setup and Results

The paper's experimental methodology involves training and evaluating object detectors on various datasets, including KITTI, BDD, and Waymo. These datasets represent different driving conditions and environments. The object detectors are initially trained on one dataset and then evaluated on another to simulate a domain shift between training and deployment environments.

Their experiments reveal that the cascaded neural network architecture outperforms existing baseline methods in predicting when the mAP falls below a specified critical threshold (0.4). The results show that their approach is highly effective, achieving true positive rates significantly higher than the baselines at low false positive rates across different datasets and object detector configurations (FRCNN and RetinaNet).

Implications and Future Work

The research emphasizes the importance of self-monitoring systems for object detection in dynamic and unpredictable environments. Practically, this contributes to the development of more robust autonomous systems that can provide feedback about their operational limits and reliability, thus enhancing both safety and performance.

Future work could explore extending this methodology to other components of robotic perception systems or integrating it with additional sensors and modalities to further improve system resilience. Additionally, the potential for real-time adaptation or re-training of the object detector using insights from the performance monitoring system could also be an avenue for further research.

Overall, the paper provides a meaningful step towards reliable autonomous systems by addressing the critical challenge of online performance monitoring, enabling systems to operate more safely across a wider range of conditions.