Evaluating Object (mis)Detection from a Safety and Reliability Perspective: Discussion and Measures

• URL: http://arxiv.org/abs/2203.02205v3
• Date: Thu, 23 Nov 2023 17:04:26 GMT
• Title: Evaluating Object (mis)Detection from a Safety and Reliability Perspective: Discussion and Measures
• Authors: Andrea Ceccarelli and Leonardo Montecchi
• Abstract summary: 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.
• Score: 1.8492669447784602
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We argue that object detectors in the safety critical domain should prioritize detection of objects that are most likely to interfere with the actions of the autonomous actor. Especially, this applies to objects that can impact the actor's safety and reliability. To quantify the impact of object (mis)detection on safety and reliability in the context of autonomous driving, we propose new object detection measures that reward the correct identification of objects that are most dangerous and most likely to affect driving decisions. To achieve this, we build an object criticality model to reward the detection of the objects based on proximity, orientation, and relative velocity with respect to the subject vehicle. Then, 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.

Related papers

• 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)
• DRUformer: Enhancing the driving scene Important object detection with driving relationship self-understanding [50.81809690183755]
  Traffic accidents frequently lead to fatal injuries, contributing to over 50 million deaths until 2023. Previous research primarily assessed the importance of individual participants, treating them as independent entities. We introduce Driving scene Relationship self-Understanding transformer (DRUformer) to enhance the important object detection task.
  arXiv  Detail & Related papers  (2023-11-11T07:26:47Z)
• Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration [17.461451218469062]
  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.
  arXiv  Detail & Related papers  (2023-07-03T11:16:39Z)
• On the Importance of Backbone to the Adversarial Robustness of Object Detectors [26.712934402914854]
  We argue that using adversarially pre-trained backbone networks is essential for enhancing the adversarial robustness of object detectors. We propose a simple yet effective recipe for fast adversarial fine-tuning on object detectors with adversarially pre-trained backbones. Our empirical results set a new milestone and deepen the understanding of adversarially robust object detection.
  arXiv  Detail & Related papers  (2023-05-27T10:26:23Z)
• USC: Uncompromising Spatial Constraints for Safety-Oriented 3D Object Detectors in Autonomous Driving [7.355977594790584]
  We consider the safety-oriented performance of 3D object detectors in autonomous driving contexts. We present uncompromising spatial constraints (USC), which characterize a simple yet important localization requirement. We incorporate the quantitative measures into common loss functions to enable safety-oriented fine-tuning for existing models.
  arXiv  Detail & Related papers  (2022-09-21T14:03:08Z)
• ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Masking [95.6347501381882]
  Object detectors are found to be vulnerable to physical-world patch hiding attacks. We propose ObjectSeeker as a framework for building certifiably robust object detectors.
  arXiv  Detail & Related papers  (2022-02-03T19:34:25Z)
• 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)
• Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection [55.12894776039135]
  State-of-the-art 3D object detectors, based on deep learning, have shown promising accuracy but are prone to over-fit to domain idiosyncrasies. We propose a novel learning approach that drastically reduces this gap by fine-tuning the detector on pseudo-labels in the target domain. We show, on five autonomous driving datasets, that fine-tuning the detector on these pseudo-labels substantially reduces the domain gap to new driving environments.
  arXiv  Detail & Related papers  (2021-03-26T01:18:11Z)
• 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)
• Towards robust sensing for Autonomous Vehicles: An adversarial perspective [82.83630604517249]
  It is of primary importance that the resulting decisions are robust to perturbations. Adversarial perturbations are purposefully crafted alterations of the environment or of the sensory measurements. A careful evaluation of the vulnerabilities of their sensing system(s) is necessary in order to build and deploy safer systems.
  arXiv  Detail & Related papers  (2020-07-14T05:25:15Z)
• Understanding Object Detection Through An Adversarial Lens [14.976840260248913]
  This paper presents a framework for analyzing and evaluating vulnerabilities of deep object detectors under an adversarial lens. We demonstrate that the proposed framework can serve as a methodical benchmark for analyzing adversarial behaviors and risks in real-time object detection systems. We conjecture that this framework can also serve as a tool to assess the security risks and the adversarial robustness of deep object detectors to be deployed in real-world applications.
  arXiv  Detail & Related papers  (2020-07-11T18:41:47Z)

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.