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Benchmarking Object Detection Robustness against Real-World Corruptions

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Abstract

With the rapid recent development, deep learning based object detection techniques have been applied to various real-world software systems, especially in safety-critical applications like autonomous driving. However, few studies are conducted to systematically investigate the robustness of state-of-the-art object detection techniques against real-world image corruptions and yet few benchmarks of object detection methods in terms of robustness are publicly available. To bridge this gap, we initiate to create a public benchmark of COCO-C and BDD100K-C, composed of sixteen real-world corruptions according to the real damages in camera sensors and image pipeline. Based on that, we further perform a systematic empirical study and evaluation of twelve representative object detectors covering three different categories of architectures (i.e., two-stage, one-stage, transformer architectures) to identify the current challenges and explore future opportunities. Our key findings include (1) the proposed real-world corruptions pose a threat to object detectors, especially for the corruptions involving colour changes, (2) a detector with a high mAP may still be vulnerable to real-world corruptions, (3) if there are potential cross-scenarios applications, the one-stage detectors are recommended, (4) when object detection architectures suffer from real-world corruptions, the effectiveness of existing robustness enhancement methods is limited, and (5) two-stage and one-stage object detection architectures are more likely to miss detect objects compared with transformer-based methods against the proposed corruptions. Our results highlight the need for designing robust object detection methods against real-world corruption and the need for more effective robustness enhancement methods for existing object detectors.

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Notes

  1. https://sites.google.com/view/real-worldbenchmark.

  2. http://www.robustvision.net/.

  3. According to Liu et al. (2020) the bounding box is considered correct only if the error rate \(err^d_{O_0} < 0.5\).

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Acknowledgements

The authors would like to thank the anonymous reviewers for their insightful comments. This work is supported partially by the National Natural Science Foundation of China (61932012, 62141215, 62372228), Science, Technology, and Innovation Commission of Shenzhen Municipality (CJGJZD20200617103001003), Canada CIFAR AI Chairs Program, the Natural Sciences and Engineering Research Council of Canada (NSERC No.RGPIN-2021-02549, No.RGPAS-2021-00034, No.DGECR-2021-00019), as well as JST-Mirai Program Grant No.JPMJMI20B8, JSPS KAKENHI Grant No.JP21H04877, No.JP23H03372, JP24K02920, and also with the support from TIER IV, Inc. and Autoware Foundation. Chunrong Fang, Jia Liu and Zhenyu Chen are the corresponding authors.

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  1. Jiawei Liu and Zhijie Wang have contributed equally to this work.

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  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Jiawei Liu, Chunrong Fang, Tongtong Bai, Xufan Zhang, Jia Liu & Zhenyu Chen

  2. University of Alberta and Alberta Machine Intelligence Institute (Amii), Edmonton, Canada

    Zhijie Wang & Lei Ma

  3. The University of Tokyo, Tokyo, Japan

    Lei Ma

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Liu, J., Wang, Z., Ma, L. et al. Benchmarking Object Detection Robustness against Real-World Corruptions. Int J Comput Vis (2024). https://doi.org/10.1007/s11263-024-02096-6

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