YOLO-KED: A Novel Framework for Rotated Object Detection in Complex Environments

Abstract

Rotated object detection aims to locate and classify objects with arbitrary orientations. In complex backgrounds, small rotated objects with limited salient features present challenges for standard backbones to extract high-quality, discriminative features. Additionally, traditional single-stage detection heads suffer from spatial prediction biases due to misalignment between classification and localization tasks. To tackle these challenges, We propose a novel network architecture, YOLO-KED, which incorporates the KAGFusion Module (KAGFM), EdgeFusion Module (EFM), and Dynamic Alignment and Rotated Detection Head (DARH) to enhance the feature extraction precision of the backbone and dynamically align the classification and localization tasks. We have also released a new dataset, termed as ICDM, specifically for rotated object detection, containing 3,214 images across 11 common categories of industrial components. Experimental results demonstrate that YOLOKED outperforms existing state-of-the-art methods across multiple detection metrics, especially in complex scenarios and small object detection tasks. ICDM is available at https://github.com/twodian/ICDM.