Surface Defect Detection With Channel-Spatial Attention Modules and Bi-Directional Feature Pyramid
The YOLOv5 network architecture prioritizes speed and efficiency, but this may limit its ability to capture intricate details of complex objects. To solve the problems of insufficient feature extraction ability and incomplete feature fusion in the YOLOv5 single-stage detection network, we propose a...
Main Authors: | Haitao Xin, Kai Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2023-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10214212/ |
Similar Items
-
Research on steel surface defect classification method based on deep learning
by: Yang Gao, et al.
Published: (2024-04-01) -
Track Fastener Defect Detection Model Based on Improved YOLOv5s
by: Xue Li, et al.
Published: (2023-07-01) -
Defect Detection for Metal Shaft Surfaces Based on an Improved YOLOv5 Algorithm and Transfer Learning
by: Bi Li, et al.
Published: (2023-04-01) -
Multi-class segmentation of navel orange surface defects based on improved DeepLabv3+
by: Yun Zhu, et al.
Published: (2024-02-01) -
BiGA-YOLO: A Lightweight Object Detection Network Based on YOLOv5 for Autonomous Driving
by: Jun Liu, et al.
Published: (2023-06-01)