期刊论文

英文

International Journal of Advanced Computer Science and Applications

2158-107X

2023

14

9

226-234

10.14569/IJACSA.2023.0140924

ACKNOWLEDGMENT This research was funded by the Postgraduate Scientific Research Innovation Project of Changsha University of Science & Technology, Grant Number CXCLY2022141, the Open Research Fund of Hunan Provincial Key Laboratory of Flexible Electronic Materials Genome Engineering, Grant Number 202019, the Open Research Fund of the Hunan Province Higher Education Key Laboratory of Modeling and Monitoring on the Near-Earth Electromagnetic Environments, Grant Number N202107, and the Postgraduate Scientific Research Innovation Project of Hunan Province, Grant Number CX20200896.

本校为第一机构

In this paper, we leverage the advantages of YOLOv5 in target detection to propose a highly accurate and lightweight network, called LAD-YOLO, for surface defect detection on aluminum profiles. The LAD-YOLO addresses the issues of computational complexity, low precision, and a large number of model parameters encountered in YOLOv5 when applied to aluminum profiles defect detection. LAD-YOLO reduces the model parameters and computation while also decreasing the model size by utilizing the ShuffleNetV2 module and depthwise separable convolution i...