Object detection algorithms particularly those based on YOLO have demonstrated remarkable efficiency in balancing speed and accuracy. However, their application in brain tumour detection remains underexplored. This study proposes RepVGG-GELAN, a novel YOLO architecture enhanced with RepVGG, a reparameterized convolutional approach for object detection tasks particularly focusing on brain tumour detection within medical images. RepVGG-GELAN leverages the RepVGG architecture to improve both speed and accuracy in detecting brain tumours. Integrating RepVGG into the YOLO framework aims to achieve a balance between computational efficiency and detection performance. This study includes a spatial pyramid pooling-based Generalized Efficient Layer Aggregation Network (GELAN) architecture which further enhances the capability of RepVGG. Experimental evaluation conducted on a brain tumour dataset demonstrates the effectiveness of RepVGG-GELAN surpassing existing RCS-YOLO in terms of precision and speed. Specifically, RepVGG-GELAN achieves an increased precision of 4.91% and an increased AP50 of 2.54% over the latest existing approach while operating at 240.7 GFLOPs. The proposed RepVGG-GELAN with GELAN architecture presents promising results establishing itself as a state-of-the-art solution for accurate and efficient brain tumour detection in medical images. The implementation code is publicly available at https://github.com/ThensiB/RepVGG-GELAN.

翻译：目标检测算法，特别是基于YOLO的算法，在平衡速度与精度方面展现出卓越效能。然而，其在脑肿瘤检测中的应用仍待深入探索。本研究提出RepVGG-GELAN，一种新型YOLO架构，通过引入重参数化卷积方法RepVGG进行增强，专门针对医学图像中的脑肿瘤检测任务。RepVGG-GELAN利用RepVGG架构提升脑肿瘤检测的速度与精度，将RepVGG集成到YOLO框架中旨在实现计算效率与检测性能的平衡。本研究包含一个基于空间金字塔池化的广义高效层聚合网络（GELAN）架构，进一步增强了RepVGG的能力。在脑肿瘤数据集上的实验评估表明，RepVGG-GELAN在精度和速度上均超越现有RCS-YOLO方法。具体而言，相较于最新方法，RepVGG-GELAN在240.7 GFLOPs运算下实现了4.91%的精度提升和2.54%的AP50提升。所提出的基于GELAN架构的RepVGG-GELAN展现出优异性能，成为医学图像中精确高效脑肿瘤检测的最先进解决方案。实现代码已公开于https://github.com/ThensiB/RepVGG-GELAN。