Mitigating Hallucinations in YOLO-based Object Detection Models: A Revisit to Out-of-Distribution Detection

• URL: http://arxiv.org/abs/2503.07330v1
• Date: Mon, 10 Mar 2025 13:42:41 GMT
• Title: Mitigating Hallucinations in YOLO-based Object Detection Models: A Revisit to Out-of-Distribution Detection
• Authors: Weicheng He, Changshun Wu, Chih-Hong Cheng, Xiaowei Huang, Saddek Bensalem,
• Abstract summary: This paper studies the underlying reasons for performance bottlenecks and proposes a methodology to improve performance fundamentally.<n>By developing a methodology to carefully synthesize an OoD dataset that semantically resembles the objects to be detected, we achieve a 88% reduction in overall hallucination error.
• Score: 8.206992765692535
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Object detection systems must reliably perceive objects of interest without being overly confident to ensure safe decision-making in dynamic environments. Filtering techniques based on out-of-distribution (OoD) detection are commonly added as an extra safeguard to filter hallucinations caused by overconfidence in novel objects. Nevertheless, evaluating YOLO-family detectors and their filters under existing OoD benchmarks often leads to unsatisfactory performance. This paper studies the underlying reasons for performance bottlenecks and proposes a methodology to improve performance fundamentally. Our first contribution is a calibration of all existing evaluation results: Although images in existing OoD benchmark datasets are claimed not to have objects within in-distribution (ID) classes (i.e., categories defined in the training dataset), around 13% of objects detected by the object detector are actually ID objects. Dually, the ID dataset containing OoD objects can also negatively impact the decision boundary of filters. These ultimately lead to a significantly imprecise performance estimation. Our second contribution is to consider the task of hallucination reduction as a joint pipeline of detectors and filters. By developing a methodology to carefully synthesize an OoD dataset that semantically resembles the objects to be detected, and using the crafted OoD dataset in the fine-tuning of YOLO detectors to suppress the objectness score, we achieve a 88% reduction in overall hallucination error with a combined fine-tuned detection and filtering system on the self-driving benchmark BDD-100K. Our code and dataset are available at: https://gricad-gitlab.univ-grenoble-alpes.fr/dnn-safety/m-hood.

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