Efficient multi-scale object detection model with space-to-depth convolution and BiFPN combined with FasterNet: a high-performance model for precise steel surface defect detection-Reference-Cited by-同舟云学术

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Efficient multi-scale object detection model with space-to-depth convolution and BiFPN combined with FasterNet: a high-performance model for precise steel surface defect detection

Published:2024-05-16 Issue:03 Volume:33 Page:
ISSN:1017-9909
Container-title:Journal of Electronic Imaging
language:
Short-container-title:J. Electron. Imag.

Author:

Su Jun¹,Zhang Heping¹,Przystupa Krzysztof²,Kochan Orest³

Affiliation:

1. Hubei University of Technology, School of Computer Science, Wuhan, China

2. Lublin University of Technology, Department of Automation, Lublin, Poland

3. Lviv Polytechnic National University, Department of Measuring-Information Technologies, Lviv, Ukraine

Publisher

SPIE-Intl Soc Optical Eng

Reference41 articles.

1. Steel Surface Defect Recognition: A Survey

2. State of the Art in Defect Detection Based on Machine Vision

3. Defect Inspection Algorithm of Metal Surface Based on Machine Vision

4. Steel defect classification with Max-Pooling Convolutional Neural Networks

5. You Only Look Once: Unified, Real-Time Object Detection

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