Scholarlink

<p>To address the dual challenges of high parameter complexity and lack of interpretability in deep neural networks, this study proposes KAN-RCNN—a novel object detection framework based on the mathematical formulation of Kolmogorov-Arnold Networks (KANs). By integrating KANs with conventional CNN architectures, comparative experiments on the PASCAL VOC 2012 benchmark dataset demonstrate that KAN-RCNN achieves: 1) 13.6% parameter reduction compared to the original Faster R-CNN baseline; 2) 1.3% improvement in detection accuracy; 3) enhanced model interpretability. Through systematic validation with 1D synthetic signals, MNIST grayscale images, and multimodal data from PASCAL VOC 2012, the experimental results confirm that KAN-RCNN maintains competitive detection performance while attaining superior computational efficiency. This research provides new methodological insights for developing efficient and interpretable computer vision models.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Research on improved fast-RCNN target detection algorithm based on Kolmogorov-Arnold network

  • 期刊论文

  • 发表日期：

    2026年1月28日

查看全文
  • Zhigang Ren,
  • Xiangjun Tang,
  • Guoquan Ren,
  • Dinghai Wu

摘要

To address the dual challenges of high parameter complexity and lack of interpretability in deep neural networks, this study proposes KAN-RCNN—a novel object detection framework based on the mathematical formulation of Kolmogorov-Arnold Networks (KANs). By integrating KANs with conventional CNN architectures, comparative experiments on the PASCAL VOC 2012 benchmark dataset demonstrate that KAN-RCNN achieves: 1) 13.6% parameter reduction compared to the original Faster R-CNN baseline; 2) 1.3% improvement in detection accuracy; 3) enhanced model interpretability. Through systematic validation with 1D synthetic signals, MNIST grayscale images, and multimodal data from PASCAL VOC 2012, the experimental results confirm that KAN-RCNN maintains competitive detection performance while attaining superior computational efficiency. This research provides new methodological insights for developing efficient and interpretable computer vision models.