YOLOLW:一个新的轻量级目标检测模型

YOLOLW:A Novel Lightweight Object Detection Model

要满足日益增长的实时移动目标检测部署需求,目前的YOLO骨干网络仍存在许多不足。为此,本文提出基于锚框的轻量级目标检测模型YOLOLW。首先,它包含一个新颖的轻量级解耦头,以增强对分类和回归任务的关注,提高模型的准确性;其次,它设计一个轻量化和重参数化的网络结构,在保持其轻量化特性的同时,实现优异的检测精度;再次,通过动态卷积和跨层次关联有效整合浅层特征,增强特征金字塔结构(FPN);最后,引入空间注意机制和通道注意机制,显著提高了模型的准确性。实验结果验证了该模型的有效性。

In response to the growing demand for real-time mobile object detection deployment,the current YOLO backbone network falls short.Hence,this paper proposes YOLOLW,a lightweight object detection model based on the anchor frame.Firstly,it incorporates a novel lightweight decoupling header to enhance focus on classification and regression tasks and improve model accuracy.Secondly,it designs a lightweight and reparameterized network structure that achieves superior detection accuracy while maintaining its lightweight nature.Thirdly,it enhances the feature pyramid structure(FPN)by effectively integrating shallow features through dynamic convolution and cross-hierarchy association.Lastly,spatial and channel attention mechanisms are introduced to significantly boost the model’s accuracy.Experimental results validate the effectiveness of the YOLOLW model.