任务(项目管理)
卷积神经网络
机制(生物学)
人机交互
计算机视觉
特征(语言学)
机器学习
任务分析
作者
Jiahui Sun,Huayong Ge,Zhehao Zhang
出处
期刊:IEEE Advanced Information Technology, Electronic and Automation Control Conference
日期:2021-03-12
卷期号:5: 1451-1456
被引量:4
标识
DOI:10.1109/iaeac50856.2021.9390855
摘要
With the development of deep learning in the field of object detection, a series of detection algorithms and methods are proposed to improve their performance. YOLOv4 is superior to the early algorithms in detection accuracy and speed. In order to get excellent performance in person detection, the AS-YOLO is proposed based on the YOLOv4 network. An attention mechanism is introduced in YOLOv4 to enhance the detection capability. Residual structure and SqueezeNet structure are also used as reference to deepen the network and reduce parameters. In addition, the activation function of backbone network is replaced by LeakyReLu to accelerate the detection speed. Compared with YOLOv4, the proposed network can decrease the parameters by 20.49% and increase the average precision by 2.02% on INRIA person data set, and the detection speed is also improved by 16.63%. This proves the validity of the proposed AS-YOLO.
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