Jul 2026· 2026 IEEE 27th China Conference on System Simulation Technology and its Applications (CCSSTA)· pp. 592-597· 0 citations· 16 references
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.
Results suggest that CFM-YOLO provides a competitive trade-off between detection accuracy and computational cost for UAV-based infrared–visible object detection and can reduce several missed detections in low-light pedestrian scenes.
Ze-Dong Huang, Kang-Kang Du, Xiao-Huang Hu et al.· Scientific Reports· 0 citations
An improved YOLOv8 lightweight detail dynamic fusion algorithm (LDDF-YOLO) is proposed, to remedy the insufficient adaptation capability of vanilla lightweight YOLOv8 in remote sensing object detection tasks and possesses robust detection performance in challenging remote sensing scenarios with dense small targets and...
S. Bo, Zheng Wei, Yiran Lu et al.· Journal of Real-Time Image P...· 1 citation
An Adaptive and Scalable YOLO model named AS-YOLOR (Adaptive and Scalable YOLO for Rotated object detection), based on the YOLOv8 baseline is proposed, providing a solution with strong practical potential for achieving efficient and high-precision detection of small, rotated objects.
Jin Huang, Juntao Shen, Min Wang et al.· Applied Sciences· 0 citations
An improved algorithm based on YOLOv12n is proposed, with a progressive and complementary design that sequentially performs detail enhancement, cross-layer fusion optimization, and final detection, confirming its stability and efficiency for small‐object detection.
Bing-Bing Fu, Jiyang Qi· Measurement science and tech...· 0 citations
An improved YOLOv8 model based on cross-attention fusion, named CAF-YOLO, which has only 11.74M parameters and maintains robust detection capability in low-light and occlusion scenarios.
Jianhong Li· Poster Volume 0007 The 2026...· 0 citations
Experimental results show that ZOS-Net achieves a favorable balance between detection performance and complexity on the M3FD, FLIR-aligned, and VEDAI datasets and provides a lightweight solution for RGB-T object detection on resource-constrained platforms.
