PIEDet: Prototype-Driven Intrinsically Explainable Object Detection

[2604.13981] PIEDet: Prototype-Driven Intrinsically Explainable Object Detection Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2604.13981 (cs) [Submitted on 15 Apr 2026 ( v1 ), last revised 15 Aug 2026 (this version, v2)] Title: PIEDet: Prototype-Driven Intrinsically Explainable Object Detection Authors: Jianlin Xiang , Linhui Dai , Xue Yang , Chaolei Yang , Yanshan Li View a PDF of the paper titled PIEDet: Prototype-Driven Intrinsically Explainable Object Detection, by Jianlin Xiang and 4 other authors View PDF HTML (experimental) Abstract: Existing object detectors typically make predictions in a black-box manner and struggle to simultaneously provide discriminative evidence for their predictions, which limits their deployment in safety-critical scenarios. To explain model predictions, existing post-hoc explanation methods mostly rely on gradient-based or perturbation-based operators. These methods not only introduce additional memory and computational overhead but also make it difficult to ensure that the generated explanations faithfully reflect the model's internal decision-making process. To address these limitations, we propose PIEDet, a prototype-driven intrinsically explainable object detection framework. PIEDet innovatively embeds class prototypes as explicit discriminative units into the classification branch of a one-stage detector, thereby improving detection performance while providing intrinsic interpretability. First, PIEDet constructs hierarchical class prototypes at different detection levels, enabling the model to learn scale-aware class-semantic representations. Second, we propose a prototype-driven feature learning method consisting of prototype regularization and a region-to-prototype matching loss. The former enhances the inter-class discriminability of the prototypes, while the latter encourages prototype responses to focus on object regions. Finally, we introduce a scale-aligned hierarchical prototype supervision mechanism that assigns scale-matched supervision signals to different detection levels, thereby enhancing the scale specificity of the hierarchical prototypes. On the ExDark, RTTS, and VOC2012-FOG datasets, PIEDet improves mAP@0.5 over the baseline by 4.7%, 1.6%, and 4.8%, respectively, while demonstrating superior computational efficiency. Compared with mainstream post-hoc explanation methods, PIEDet achieves a better balance between explanation quality and explanation cost. Comments: 14 pages, 6 figures Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2604.13981 [cs.CV] (or arXiv:2604.13981v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2604.13981 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jianlin Xiang [ view email ] [v1] Wed, 15 Apr 2026 15:26:21 UTC (15,358 KB) [v2] Sat, 15 Aug 2026 02:40:09 UTC (12,663 KB) Full-text links: Access Paper: View a PDF of the paper titled PIEDet: Prototype-Driven Intrinsically Explainable Object Detection, by Jianlin Xiang and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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