\(\eta\)-repyolo: real-time object detection method based on \(\eta\)-RepConv and YOLOv8

Deep learning-based object detection methods often grapple with excessive model parameters, high complexity, and subpar real-time performance. In response, the YOLO series, particularly the YOLOv5s to YOLOv8s methods, has been developed by scholars to strike a balance between real-time processing and accuracy. Nevertheless, YOLOv8’s precision can fall short in certain specific applications. To address this, we introduce a real-time object detection method called \(\eta\) $\eta$ -RepYOLO, which is built upon the \(\eta\) $\eta$ -RepConv structure. This method is designed to maintain consistent detection speeds while improving accuracy. We begin by crafting a backbone network named \(\eta\) $\eta$ -EfficientRep, which utilizes a strategically designed network unit- \(\eta\) $\eta$ -RepConv and \(\eta\) $\eta$ -RepC2f module, to reparameterize and subsequently generate an efficient inference model. This model achieves superior performance by extracting detailed feature maps from images. Subsequently, we propose the enhanced \(\eta\) $\eta$ -RepPANet and \(\eta\) $\eta$ -RepAFPN as the model’s detection neck, with the addition of the \(\eta\) $\eta$ -RepC2f for optimized feature fusion, thus boosting the neck’s functionality. Our innovation continues with the development of an advanced decoupled head for detection, where the \(\eta\) $\eta$ -RepConv takes the place of the traditional \(3 \times 3\) $3\times 3$ conv, resulting in a marked increase in detection precision during the inference stage. Our proposed \(\eta\) $\eta$ -RepYOLO method, when applied to distinct neck modules, \(\eta\) $\eta$ -RepPANet and \(\eta\) $\eta$ -RepAFPN, achieves mAP of 84.77%/85.65% on the PASCAL VOC07+12 dataset and AP of 45.3%/45.8% on the MSCOCO dataset, respectively. These figures represent a significant advancement over the YOLOv8s method. Additionally, the model parameters for \(\eta\) $\eta$ -RepYOLO are reduced to 10.8M/8.8M, which is 3.6%/21.4% less than that of YOLOv8, culminating in a more streamlined detection model. The detection speeds clocked on an RTX3060 are 116 FPS/81 FPS, showcasing a substantial enhancement in comparison to YOLOv8s. In summary, our approach delivers competitive performance and presents a more lightweight alternative to the SOTA YOLO models, making it a robust choice for real-time object detection applications.