首页|YOLOLW:一个新的轻量级目标检测模型

YOLOLW:一个新的轻量级目标检测模型

扫码查看
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.
YOLOLW:A Novel Lightweight Object Detection Model
In response to the growing demand for real-time mobile object detection deployment,the current YOLO backbone net-work 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 shal-low 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.

target detectionYOLOLWlightweight modelattention mechanismFPN

张宇、黎靖、马铭、王众祥、孙妍

展开 >

沈阳化工大学计算机科学与技术学院,辽宁 沈阳 110142

辽宁省化工过程工业智能化技术重点实验室,辽宁 沈阳 110142

目标检测 YOLOLW 轻量化模型 注意力机制 FPN

2024

计算机与现代化
江西省计算机学会 江西省计算技术研究所

计算机与现代化

CSTPCD
影响因子:0.472
ISSN:1006-2475
年,卷(期):2024.(11)