YOLO11-4K: An Efficient Architecture for Real-Time Small Object Detection in 4K Panoramic Images
Huma Hafeez, Matthew Garratt, Jo Plested, Sankaran Iyer, Arcot Sowmya

TL;DR
YOLO11-4K is a novel real-time detection architecture optimized for 4K panoramic images, combining a multi-scale detection head and GhostConv backbone to enhance small object detection and computational efficiency.
Contribution
The paper introduces YOLO11-4K, a new architecture with a multi-scale detection head and GhostConv backbone, tailored for high-resolution panoramic images, and provides a new annotated dataset for evaluation.
Findings
Achieves 0.95 mAP at 28.3 ms per frame
Reduces latency by 75% compared to YOLO11
Improves small object detection in 4K panoramic images
Abstract
The processing of omnidirectional 360-degree images poses significant challenges for object detection due to inherent spatial distortions, wide fields of view, and ultra-high-resolution inputs. Conventional detectors such as YOLO are optimised for standard image sizes (for example, 640x640 pixels) and often struggle with the computational demands of 4K or higher-resolution imagery typical of 360-degree vision. To address these limitations, we introduce YOLO11-4K, an efficient real-time detection framework tailored for 4K panoramic images. The architecture incorporates a novel multi-scale detection head with a P2 layer to improve sensitivity to small objects often missed at coarser scales, and a GhostConv-based backbone to reduce computational complexity without sacrificing representational power. To enable evaluation, we manually annotated the CVIP360 dataset, generating 6,876…
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Taxonomy
TopicsAdvanced Neural Network Applications · Advanced Image and Video Retrieval Techniques · Visual Attention and Saliency Detection
