保险丝(电气)
计算机科学
人工智能
图层(电子)
领域(数学分析)
网络结构
草根
数据挖掘
模式识别(心理学)
算法
机器学习
数学
工程类
数学分析
法学
化学
有机化学
电气工程
政治
政治学
作者
Xiangping Zhang,Honghui Fan,Hongjin Zhu,Xianzhen Huang,Tao Wu,Heng Zhou
标识
DOI:10.1109/ccis53392.2021.9754600
摘要
In this paper, we propose an improved model DAN-YOLOV5 based on YOLOV5. First, we use a mosaic enhancement strategy, which creates a large number of new samples on the existing VOC2007 dataset. Second, an innovative adaptive network module DAN is used on top of YOLOV5. The adaptive network module DAN is used to fuse features from same-layer scenes or cross-layer scenes. Finally, the experimental results show that the accuracy of the YOLOV5 dataset enhanced with shear-mixing and mosaic enhancement strategies is 71.02%, which is 13.56% better than the unenhanced data, and the average accuracy Figure is 80.05%, which is 33.11 percentage points better than the data. Applying the adaptive network module DAN to the YOLOV5 model, it improves the accuracy by 2.61% relative to YOLOV5 at 75.28%. Achieving such experimental results without increasing the computational effort and complexity at the grassroots level is well worth studying.
科研通智能强力驱动
Strongly Powered by AbleSci AI