GBCD-YOLO: A High-Precision and Real-Time Lightweight Model for Wood Defect Detection
With the advancement of the wood processing industry, the demand for the detection of surface defects in wood has become increasingly urgent. The application of automated production technology has enhanced the efficiency and precision of wood processing, which can significantly impact product qualit...
Main Authors: | Yunchang Zheng, Mengfan Wang, Bo Zhang, Xiangnan Shi, Qing Chang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10409188/ |
Similar Items
-
ODCA-YOLO: An Omni-Dynamic Convolution Coordinate Attention-Based YOLO for Wood Defect Detection
by: Rijun Wang, et al.
Published: (2023-09-01) -
An Improved YOLOv5 Algorithm for Wood Defect Detection Based on Attention
by: Siyu Han, et al.
Published: (2023-01-01) -
LCG-YOLO: A Real-Time Surface Defect Detection Method for Metal Components
by: Jiangli Yu, et al.
Published: (2024-01-01) -
CCG-YOLOv7: A Wood Defect Detection Model for Small Targets Using Improved YOLOv7
by: Wenqi Cui, et al.
Published: (2024-01-01) -
SGN-YOLO: Detecting Wood Defects with Improved YOLOv5 Based on Semi-Global Network
by: Wei Meng, et al.
Published: (2023-10-01)