YOLO26 vs. YOLOv8: A Comprehensive Architectural Benchmark of Next-Generation Real-Time Object Detection Models
This paper presents a rigorous empirical evaluation of Ultralytics YOLO26 against the YOLOv8 baseline, offering an independent real-world stress test of NMS-free architectures on non-COCO distributions. Engineered for edge deployment, YOLO26 introduces native end-to-end one-to-one label assignment, the removal of Distribution Focal Loss (DFL), and a spectral-constrained CSP-Muon backbone. We conducted a comprehensive, cross-scale comparative analysis across five model capacities, using the general object detection (Pascal VOC) and dense aerial small-object detection (VisDrone) datasets. Models are evaluated across accuracy (mAP_50 and mAP_50:95), model complexity, and hardware-specific CP...
| Authors | Chidera G. Oguine, Kanyifeechukwu J. Oguine, Obiozor M. Oguine et al. |
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| Primary category | cs.CV |
| Published | 24 May 2026 |
| Last updated at source | 19 June 2026 |
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