Real-time defect detection systems for steel and wood inspection

Automated inspection of raw materials for steel and wood surfaces in manufacturing settings is experiencing unprecedented development thanks to the integration of vision-based systems using object detection models. Considering time efficiency, most current surface defect detection approaches are bas...

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Bibliographic Details
Published in2024 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) pp. 577 - 582
Main Authors Ghali, Rayen, Benhafid, Zhor, Selouani, Sid Ahmed
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.08.2024
Subjects
Accuracy
DETR
Feature extraction
Inspection
Object detection
Raw materials
Real-time systems
Surface defects
Transformers
YOLO
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Summary:Automated inspection of raw materials for steel and wood surfaces in manufacturing settings is experiencing unprecedented development thanks to the integration of vision-based systems using object detection models. Considering time efficiency, most current surface defect detection approaches are based on YOLOv5 (You Only Look Once, fifth version). Meanwhile, the state-of-the-art in real-time object detection has largely evolved, and the more recent systems have yet to be evaluated for surface defect detection. Thus, this paper presents the results of an extensive effectiveness assessment of YOLOv5, YOLOv6 v3.0, YOLOv7, YOLOv8, YOLOv9, and Real-Time Detection Transformer (RT-DETR) by evaluating the trade-offs between inference speed and accuracy reached by these models for defect detection. Experimental results show that YOLOv6 v3.0 maintains the highest level of combined performance when considering both mean Average Precision and Frames Per Second metrics and using industrial datasets of wood and steel defects.
ISSN:2576-7046
DOI:10.1109/CCECE59415.2024.10667164