With an excellent balance between speed and accuracy, cutting-edge YOLO frameworks have become one of the most efficient algorithms for object detection. However, the performance of using YOLO networks is scarcely investigated in brain tumor detection. We propose a novel YOLO architecture with Reparameterized Convolution based on channel Shuffle (RCS-YOLO). We present RCS and a One-Shot Aggregation of RCS (RCS-OSA), which link feature cascade and computation efficiency to extract richer information and reduce time consumption. Experimental results on the brain tumor dataset Br35H show that the proposed model surpasses YOLOv6, YOLOv7, and YOLOv8 in speed and accuracy. Notably, compared with YOLOv7, the precision of RCS-YOLO improves by 2.6%, and the inference speed by 60% at 114.8 images detected per second (FPS). Our proposed RCS-YOLO achieves state-of-the-art performance on the brain tumor detection task. The code is available at https://github.com/mkang315/RCS-YOLO.

翻译：在速度与精度之间取得优秀平衡的先进YOLO框架已成为目标检测领域最高效的算法之一。然而，YOLO网络在脑肿瘤检测中的性能研究尚不充分。我们提出了一种基于通道混洗的重新参数化卷积的新型YOLO架构（RCS-YOLO）。我们设计了RCS模块及其单次聚合结构（RCS-OSA），通过连接特征级联与计算效率，在提取更丰富信息的同时降低时间消耗。在脑肿瘤数据集Br35H上的实验结果表明，所提模型在速度和精度上均超越YOLOv6、YOLOv7和YOLOv8。值得注意的是，与YOLOv7相比，RCS-YOLO的精度提升了2.6%，推理速度（以每秒检测图像数FPS计）提升60%至114.8张/秒。我们提出的RCS-YOLO在脑肿瘤检测任务中达到了最先进的性能。代码开源地址为https://github.com/mkang315/RCS-YOLO。