SCD-YOLO: An Improved Visible-Infrared Object Detection Algorithm for YOLOv11

Abstract

Single-modal object detection methods often suffer from low accuracy, false detections, and missed detections in complex scenes such as low-light and foggy environments. To address these problems, this paper proposes SCD-YOLO, a visible-infrared dual-modal object detection network based on YOLOv11n. First, a spatial-channel decoupled fusion module, named SCD-Fusion, is designed to enhance cross-modal feature interaction. This module consists of a cross-directional spatial enhancement module (CDSE) and a decoupled channel fusion module (BCDF). CDSE models long-range spatial dependencies along the height and width directions, while BCDF captures differential and common channel information between visible and infrared features. In addition, a cascaded adaptive spatial detection head, CAS-Head, is introduced to progressively fuse multi-scale features and adaptively weight four-scale features. Experimental results on the FLIR dataset show that SCD-YOLO achieves 75.23% mAP@0.5 and 40.89% mAP@0.5:0.95, which are 4.13% and 3.84% higher than those of the baseline YOLOv11n model, respectively.