🤖 AI Summary
This work proposes an efficient object detection architecture based on YOLOv11 to address the challenges of weak small-object detection and insufficient feature representation in real-time applications. By integrating C3K2 modules into the backbone, SPPF structures into the neck, and C2PSA modules with spatial attention into the detection head, the model enhances multi-scale feature fusion and spatial detail perception. The proposed approach significantly improves mean average precision (mAP), particularly for small objects, while maintaining high inference speed. These advancements make the method well-suited for latency-sensitive real-world scenarios such as autonomous driving and video surveillance.
📝 Abstract
YOLOv11 is the latest iteration in the You Only Look Once (YOLO) series of real-time object detectors, introducing novel architectural modules to improve feature extraction and small-object detection. In this paper, we present a detailed analysis of YOLOv11, including its backbone, neck, and head components. The model key innovations, the C3K2 blocks, Spatial Pyramid Pooling - Fast (SPPF), and C2PSA (Cross Stage Partial with Spatial Attention) modules enhance spatial feature processing while preserving speed. We compare YOLOv11 performance to prior YOLO versions on standard benchmarks, highlighting improvements in mean Average Precision (mAP) and inference speed. Our results demonstrate that YOLOv11 achieves superior accuracy without sacrificing real-time capabilities, making it well-suited for applications in autonomous driving, surveillance, and video analytics.This work formalizes YOLOv11 in a research context, providing a clear reference for future studies.