Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration

• URL: http://arxiv.org/abs/2307.00934v1
• Date: Mon, 3 Jul 2023 11:16:39 GMT
• Title: Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration
• Authors: Kemal Oksuz and Tom Joy and Puneet K. Dokania
• Abstract summary: In this work, we introduce the Self-Aware Object Detection (SAOD) task. The SAOD task respects and adheres to the challenges that object detectors face in safety-critical environments such as autonomous driving. We extensively use our framework, which introduces novel metrics and large scale test datasets, to test numerous object detectors.
• Score: 17.461451218469062
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The current approach for testing the robustness of object detectors suffers from serious deficiencies such as improper methods of performing out-of-distribution detection and using calibration metrics which do not consider both localisation and classification quality. In this work, we address these issues, and introduce the Self-Aware Object Detection (SAOD) task, a unified testing framework which respects and adheres to the challenges that object detectors face in safety-critical environments such as autonomous driving. Specifically, the SAOD task requires an object detector to be: robust to domain shift; obtain reliable uncertainty estimates for the entire scene; and provide calibrated confidence scores for the detections. We extensively use our framework, which introduces novel metrics and large scale test datasets, to test numerous object detectors in two different use-cases, allowing us to highlight critical insights into their robustness performance. Finally, we introduce a simple baseline for the SAOD task, enabling researchers to benchmark future proposed methods and move towards robust object detectors which are fit for purpose. Code is available at https://github.com/fiveai/saod

Related papers

• Seamless Detection: Unifying Salient Object Detection and Camouflaged Object Detection [73.85890512959861]
  We propose a task-agnostic framework to unify Salient Object Detection (SOD) and Camouflaged Object Detection (COD) We design a simple yet effective contextual decoder involving the interval-layer and global context, which achieves an inference speed of 67 fps. Experiments on public SOD and COD datasets demonstrate the superiority of our proposed framework in both supervised and unsupervised settings.
  arXiv  Detail & Related papers  (2024-12-22T03:25:43Z)
• Uncertainty Estimation for 3D Object Detection via Evidential Learning [63.61283174146648]
  We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections.
  arXiv  Detail & Related papers  (2024-10-31T13:13:32Z)
• Object criticality for safer navigation [1.565361244756411]
  Given an object detector, filtering objects based on their relevance, reduces the risk of missing relevant objects, decreases the likelihood of dangerous trajectories, and improves the quality of trajectories in general. We show that, given an object detector, filtering objects based on their relevance, reduces the risk of missing relevant objects, decreases the likelihood of dangerous trajectories, and improves the quality of trajectories in general.
  arXiv  Detail & Related papers  (2024-04-25T09:02:22Z)
• On the Importance of Backbone to the Adversarial Robustness of Object Detectors [26.574691350231348]
  Object detection is a critical component of various security-sensitive applications, such as autonomous driving and video surveillance. Existing object detectors are vulnerable to adversarial attacks, which poses a significant challenge to their reliability and security. We propose a simple yet effective recipe for fast adversarial fine-tuning on object detectors with adversarially pre-trained backbones.
  arXiv  Detail & Related papers  (2023-05-27T10:26:23Z)
• Evaluating Object (mis)Detection from a Safety and Reliability Perspective: Discussion and Measures [1.8492669447784602]
  We propose new object detection measures that reward the correct identification of objects that are most dangerous and most likely to affect driving decisions. We apply our model on the recent autonomous driving dataset nuScenes, and we compare nine object detectors. Results show that, in several settings, object detectors that perform best according to the nuScenes ranking are not the preferable ones when the focus is shifted on safety and reliability.
  arXiv  Detail & Related papers  (2022-03-04T09:31:20Z)
• CertainNet: Sampling-free Uncertainty Estimation for Object Detection [65.28989536741658]
  Estimating the uncertainty of a neural network plays a fundamental role in safety-critical settings. In this work, we propose a novel sampling-free uncertainty estimation method for object detection. We call it CertainNet, and it is the first to provide separate uncertainties for each output signal: objectness, class, location and size.
  arXiv  Detail & Related papers  (2021-10-04T17:59:31Z)
• Pseudo-IoU: Improving Label Assignment in Anchor-Free Object Detection [60.522877583407904]
  Current anchor-free object detectors are quite simple and effective yet lack accurate label assignment methods. We present Pseudo-Intersection-over-Union(Pseudo-IoU): a simple metric that brings more standardized and accurate assignment rule into anchor-free object detection frameworks. Our method achieves comparable performance to other recent state-of-the-art anchor-free methods without bells and whistles.
  arXiv  Detail & Related papers  (2021-04-29T02:48:47Z)
• Robust Object Detection via Instance-Level Temporal Cycle Confusion [89.1027433760578]
  We study the effectiveness of auxiliary self-supervised tasks to improve the out-of-distribution generalization of object detectors. Inspired by the principle of maximum entropy, we introduce a novel self-supervised task, instance-level temporal cycle confusion (CycConf) For each object, the task is to find the most different object proposals in the adjacent frame in a video and then cycle back to itself for self-supervision.
  arXiv  Detail & Related papers  (2021-04-16T21:35:08Z)
• Slender Object Detection: Diagnoses and Improvements [74.40792217534]
  In this paper, we are concerned with the detection of a particular type of objects with extreme aspect ratios, namely textbfslender objects. For a classical object detection method, a drastic drop of $18.9%$ mAP on COCO is observed, if solely evaluated on slender objects.
  arXiv  Detail & Related papers  (2020-11-17T09:39:42Z)
• Online Monitoring of Object Detection Performance During Deployment [6.166295570030645]
  We 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. We evaluate our proposed approach using different combinations of autonomous driving datasets and object detectors.
  arXiv  Detail & Related papers  (2020-11-16T07:01:43Z)
• Multivariate Confidence Calibration for Object Detection [7.16879432974126]
  We present a novel framework to measure and calibrate biased confidence estimates of object detection methods. Our approach allows, for the first time, to obtain calibrated confidence estimates with respect to image location and box scale. We show that our developed methods outperform state-of-the-art calibration models for the task of object detection.
  arXiv  Detail & Related papers  (2020-04-28T14:17:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.