摘要
为了避免人们边行走边使用手机发生危险,本文提出了实时性强的轻量级模型(Mobile-YOLOv3)来检测路面障碍.我们在广州各地拍摄路障图片并标注了一个路障数据集,使用了一个轻量级的MobileNetv1网络来替换YOLOv3的骨干网络实现轻量化,并且应用了4个方法用于提高检测精度和模型的鲁棒型.4个方法分别为:边框回归损失函数CIOU、分类损失函数Focal、预测框筛选算法Soft-NMS、负样本训练.实验结果证明,该模型获得了98.84%的MAP.与YOLOv3对比,该模型的规模缩减了2.5倍,检测精度却提高了7%.
To avoid the danger of people using mobile phones while walking,this study proposes a lightweight model(Mobile-YOLOv3)with strong real-time performance to detect road obstacles.We photograph roadblocks and annotate a roadblock data set around Guangzhou City.Lightweight is achieved by the replacement of the backbone network of YOLOv3 with a lightweight MobileNetv1 network.In addition,we apply four methods to improve detection accuracy and model robustness,i.e.,border regression loss function CIOU,classification loss function Focal,prediction box screening algorithm Soft-NMS,and negative sample training.The experimental results show that the model obtains 98.84%MAP.Compared with YOLOv3,this model has the scale reduced by 2.5 times but the detection accuracy improved by 7%.
作者
齐永康
QI Yong-Kang(School of Computer Science,South China Normal University,Guangzhou 510631,China)
出处
《计算机系统应用》
2022年第2期176-184,共9页
Computer Systems & Applications
