轻量级模型G-YOLOv11高效检测儿童手腕骨折，推理快且体积小。
Lightweight G-YOLOv11: Advancing Efficient Fracture Detection in Pediatric Wrist X-rays
- 用鬼魂卷积替代传统卷积，减少计算量同时保持特征提取能力。
- 在GRAZPEDWRI-DX数据集上达到[email protected] 0.535，单次推理仅需2.4毫秒。
- 相比标准YOLOv11l，模型大小缩小68.7%，适合临床边缘设备部署。
计算机辅助诊断（CAD）系统显著提升了放射科医生和外科医生对医学影像的解读效率。然而，现有用于X光片骨折检测的CAD系统大多依赖大型、资源密集型检测器，限制了其在临床中的实际应用。为此，本文提出一种基于YOLO检测器家族最新版本的轻量级CAD系统——基于鬼魂卷积的YOLOv11（G-YOLOv11），通过引入鬼魂卷积进行特征提取。该操作生成与传统卷积相同数量的特征图，但所需线性运算更少，从而降低计算资源需求。我们在GRAZPEDWRI-DX数据集上评估了G-YOLOv11的性能，其[email protected]达到0.535，单次推理时间仅为2.4毫秒（NVIDIA A10 GPU）。相较于标准YOLOv11l，G-YOLOv11l的模型尺寸减少68.7%，仅损失13.6%的[email protected]。结果表明，该系统在效率方面达到新基准，优于现有检测器。代码与模型已开源：https://github.com/AbdesselamFerdi/G-YOLOv11。
原文摘要 · Abstract (English)
Computer-aided diagnosis (CAD) systems have greatly improved the interpretation of medical images by radiologists and surgeons. However, current CAD systems for fracture detection in X-ray images primarily rely on large, resource-intensive detectors, which limits their practicality in clinical settings. To address this limitation, we propose a novel lightweight CAD system based on the YOLO detector for fracture detection. This system, named ghost convolution-based YOLOv11 (G-YOLOv11), builds on the latest version of the YOLO detector family and incorporates the ghost convolution operation for feature extraction. The ghost convolution operation generates the same number of feature maps as traditional convolution but requires fewer linear operations, thereby reducing the detector's computational resource requirements. We evaluated the performance of the proposed G-YOLOv11 detector on the GRAZPEDWRI-DX dataset, achieving an [email protected] of 0.535 with an inference time of 2.4 ms on an NVIDIA A10 GPU. Compared to the standard YOLOv11l, G-YOLOv11l achieved reductions of 13.6% in [email protected] and 68.7% in size. These results establish a new state-of-the-art benchmark in terms of efficiency, outperforming existing detectors. Code and models are available at https://github.com/AbdesselamFerdi/G-YOLOv11.
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