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 this https URL

该研究提出了一种基于重参数化卷积和通道洗牌的新型 YOLO 架构（RCS-YOLO），并通过特征级联和计算效率提取更丰富的信息，减少时间消耗，实现了在大脑肿瘤检测任务上的最新性能，超过了 YOLOv6、YOLOv7 和 YOLOv8 的速度和准确性。