A high-precision and real-time lightweight detection model for small defects in cold-rolled steel DOI

Shuzong Chen,

Shengquan Jiang,

Xiaoyu Wang

et al.

Journal of Real-Time Image Processing, Journal Year: 2024, Volume and Issue: 22(1)

Published: Dec. 23, 2024

Language: Английский

A comparison of the corrosion inhibition performance in sulfamic acid medium between refluxed and ultrasonic extracts of rapeseed meal DOI

Simei Yang,

Shuduan Deng,

Qing Qu

et al.

Journal of Colloid and Interface Science, Journal Year: 2025, Volume and Issue: 688, P. 714 - 735

Published: Feb. 27, 2025

Language: Английский

Citations

2

Fault diagnosis method of mining vibrating screen mesh based on an improved algorithm DOI Creative Commons

Fusheng Niu,

Jiahui Wu, Jinxia Zhang

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 147, P. 110343 - 110343

Published: Feb. 26, 2025

Language: Английский

Citations

1

Multi-scale defect detection on product surface in grayscale images using YOLOv8-Ms algorithm DOI
Xu Liu, Haibo Liu, Deqiang Zhou

et al.

Nondestructive Testing And Evaluation, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 25

Published: March 23, 2025

Surface defect detection is crucial in engineering quality control. While existing single-stage and two-stage models have advanced the field, conventional object algorithms still face challenges detecting multi-scale defects accurately representing features greyscale images due to their monotonicity. To address these issues, this paper proposes YOLOv8-MS, an enhanced YOLOv8 model for efficient accurate surface detection. We use convolutions design MSC2f module adapt at different scales. A novel GFPN introduced information fusion, a multi-information attention further boosts network's performance. handle single-channel nature of images, we develop input preprocessing method. Experiments on three open-source datasets show that our achieves mean average precision (mAP) 44.7% NEU-DET, 4.1% improvement over YOLOv8, outperforms YOLOv5 other datasets. Moreover, inference time not significantly increased, effectively balancing accuracy efficiency.

Language: Английский

Citations

0

Lightweight defect detection network based on steel strip raw images DOI
Yue Huang, Zhen Chen, Zhaoxiang Chen

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 145, P. 110179 - 110179

Published: Feb. 6, 2025

Language: Английский

Citations

0

Multisource data-driven intelligent method for detecting surface defects in cold-rolled copper strips DOI

Bowei Duan,

Dongcheng Wang, Yongsheng Ma

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 152, P. 110730 - 110730

Published: April 11, 2025

Language: Английский

Citations

0

AWBN-YOLO: A Surface Defect Detection Method for Aero-Engine Blades in Sample-Limited Scenarios DOI
Wei Gao,

Nengbin Lv,

Fuzhou Du

et al.

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: May 8, 2025

Abstract In the production process of aero-engine blades (AEBs), surface defect detection is essential. However, data scarcity and class imbalance in practical industrial scenarios make deep learning-based identification AEBs challenging. this article, we propose AWBN-YOLO, an enhanced YOLOv10n-based framework to address these challenges. Specifically, design Adaptive Sample Augmentation Method (ASAM) synthesize photorealistic samples by adaptively aligning geometries with blade contours optimizing background consistency. We also a Feature-driven Wavelet Downsampling (FWD) module preserve critical spatial-frequency details through adaptive wavelet basis selection, enhancing sensitivity fine-grained defects. Furthmore, introduce BiFPN-Concat Normalized Wasserstein Distance Loss (NWD-Loss) optimize multi-scale feature fusion small-defect localization. Experiments on AeBAD-SL dataset, sample-imbalanced benchmark for have proven that AWBN-YOLO can achieve state-of-the-art performance 82.2% precision, 71.9% recall, 71.7% mAP50, surpassing baseline YOLOv10n 2.8%, 1.2%, 2.6%, respectively. achieves superior accuracy while maintaining real-time (140 FPS), offering robust solution quality inspection under constraints.

Language: Английский

Citations

0

A high-precision and real-time lightweight detection model for small defects in cold-rolled steel DOI

Shuzong Chen,

Shengquan Jiang,

Xiaoyu Wang

et al.

Journal of Real-Time Image Processing, Journal Year: 2024, Volume and Issue: 22(1)

Published: Dec. 23, 2024

Language: Английский

Citations

0