Multivariate Confidence Calibration for Object Detection

Abstract: Unbiased confidence estimates of neural networks are crucial especially for safety-critical applications. Many methods have been developed to calibrate biased confidence estimates. Though there is a variety of methods for classification, the field of object detection has not been addressed yet. Therefore, we present a novel framework to measure and calibrate biased (or miscalibrated) confidence estimates of object detection methods. The main difference to related work in the field of classifier calibration is that we also use additional information of the regression output of an object detector for calibration. Our approach allows, for the first time, to obtain calibrated confidence estimates with respect to image location and box scale. In addition, we propose a new measure to evaluate miscalibration of object detectors. Finally, we show that our developed methods outperform state-of-the-art calibration models for the task of object detection and provides reliable confidence estimates across different locations and scales.

• The paper introduces a novel framework that integrates both classification and regression outputs, significantly reducing calibration errors in object detection models.
• The study leverages independent and dependent logistic and beta calibrations to account for bounding box dimensions, improving calibration fidelity.
• Experimental evaluations on models like SSD and Faster R-CNN demonstrate that incorporating location and scale data notably enhances detection reliability.

Multivariate Confidence Calibration for Object Detection

Introduction

The paper "Multivariate Confidence Calibration for Object Detection" introduces a novel framework aimed at enhancing the calibration of confidence estimates in object detection models. Traditional classifier calibration methods, such as Platt scaling and temperature scaling, do not account for dependencies relating to an object’s location and scale. The paper presents a framework to incorporate these contextual elements, enhancing calibration, particularly critical in safety-centered applications like autonomous driving where precise detection is paramount.

Methodology

The proposed framework diverges from standard calibration methods by integrating both classification and regression outputs from an object detector, thereby facilitating what the authors term as "box-sensitive" or "multivariate" calibration. The framework is implemented post-training, treating the detector as a black-box, which means it does not modify existing detection models but enhances the calibration by utilizing bounding box data.

Calibration Techniques:

1. Independent Logistic Calibration: Extends Platt scaling to object detection, optimizing a vector rather than a scalar to account for location- and scale-sensitive elements.
2. Independent Beta Calibration: Implements a similar extension using beta distributions over bounding box dimensions, reparametrized for computational feasibility.
3. Dependent Logistic and Beta Calibration: Utilizes multivariate distributions to improve calibration fidelity, particularly in capturing correlations between confidence, location, and scale.

Measuring Miscalibration

The paper introduces an adaptation of the expected calibration error (ECE), termed Detection ECE (D-ECE), tailored for object detection tasks. This measure integrates box properties to assess discrepancies between predicted confidences and true precision, accommodating spatial and scalar dimensions of object detections.

Experimental Evaluations

Experiments were conducted using a variety of pre-trained models (SSD, Faster R-CNN, R-FCN) across the COCO dataset. The results demonstrated that the multivariate logistic and beta calibration methods significantly enhance calibration accuracy, reducing the D-ECE score by incorporating location and scale data. Notably, while traditional histogram binning showed some success, its representational power diminished with higher dimensionalities compared to the proposed multivariate methods.

Performance Metrics:

• Incorporation of bounding box dimensions led to better calibration results, especially in safety-critical zones of images such as edges and towards the scale threshold.
• Dependent logistic and beta calibrations generally achieved superior outcomes when compared to independent methods, underscoring the importance of capturing variable dependencies.

Conclusion

The paper effectively argues and demonstrates that integrating bounding box information into the calibration process can significantly optimize the reliability of confidence estimates in object detection models. The proposed framework is shown to outperform existing methods by harnessing multivariate dependencies, offering a crucial advancement for applications where precision and reliability are paramount.

Future directions may include further refining these frameworks for additional object detectors and exploring new metrics to measure and enhance calibration fidelity. The paper emphasizes the robustness of the proposed model, adaptable across different object detection methodologies, potentially setting a new standard in the field of AI-driven safety applications.