The interpretable object detection capabilities of a novel Kolmogorov-Arnold network framework are examined here. The approach refers to a key limitation in computer vision for autonomous vehicles perception, and beyond. These systems offer limited transparency regarding the reliability of their confidence scores in visually degraded or ambiguous scenes. To address this limitation, a Kolmogorov-Arnold network is employed as an interpretable post-hoc surrogate to model the trustworthiness of the You Only Look Once (Yolov10) detections using seven geometric and semantic features. The additive spline-based structure of the Kolmogorov-Arnold network enables direct visualisation of each feature's influence. This produces smooth and transparent functional mappings that reveal when the model's confidence is well supported and when it is unreliable. Experiments on both Common Objects in Context (COCO), and images from the University of Bath campus demonstrate that the framework accurately identifies low-trust predictions under blur, occlusion, or low texture. This provides actionable insights for filtering, review, or downstream risk mitigation. Furthermore, a bootstrapped language-image (BLIP) foundation model generates descriptive captions of each scene. This tool enables a lightweight multimodal interface without affecting the interpretability layer. The resulting system delivers interpretable object detection with trustworthy confidence estimates. It offers a powerful tool for transparent and practical perception component for autonomous and multimodal artificial intelligence applications.

翻译：本文研究了一种新型Kolmogorov-Arnold网络框架的可解释目标检测能力。该方案针对自动驾驶车辆感知及其他计算机视觉系统中的关键局限性——在视觉退化或模糊场景中，这些系统关于置信度得分的可靠性缺乏透明性。为解决该局限性，采用Kolmogorov-Arnold网络作为可解释的事后替代模型，利用七种几何与语义特征对YOLOv10检测结果的可信度进行建模。Kolmogorov-Arnold网络的加性样条基结构支持直接可视化各特征的影响，生成平滑且透明的函数映射，从而揭示模型置信度何时可靠、何时不可靠。在COCO数据集及巴斯大学校园图像上的实验表明，该框架能够准确识别因模糊、遮挡或低纹理导致的低可信度预测，为过滤、审查或下游风险缓解提供了可操作的见解。此外，采用引导式语言-图像基础模型生成场景描述性文本，该工具在不影响可解释性层的前提下实现了轻量级多模态接口。最终系统在提供可解释目标检测的同时，具备可信的置信度评估能力，为自主与多模态人工智能应用提供了透明且实用的感知组件。