A lightweight model for digital printing fabric defect detection based on YOLOX

Article Computer Science, Artificial Intelligence

A detection algorithm for cherry fruits based on the improved YOLO-v4 model

Rongli Gai et al.

Summary: Digital agriculture has a significant impact on the value of agricultural output. Robotic picking and cherry fruit detection technologies enable more intelligent and productive farming practices.

NEURAL COMPUTING & APPLICATIONS (2023)

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Article Materials Science, Textiles

Attention-based Feature Fusion Generative Adversarial Network for yarn-dyed fabric defect detection

Hongwei Zhang et al.

Summary: In this article, an Attention-based Feature Fusion Generative Adversarial Network framework is proposed for the unsupervised defect detection of yarn-dyed fabrics. The framework utilizes a modified Feature Pyramid Network to fuse multi-level information and an attention mechanism to enhance feature representation capabilities. Experimental results demonstrate the effectiveness of the proposed method compared to other methods on public datasets.

TEXTILE RESEARCH JOURNAL (2023)

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Article Computer Science, Artificial Intelligence

Metal Surface Defect Detection Using Modified YOLO

Yiming Xu et al.

Summary: The improved metal surface defect detection technology based on the YOLO model enhances feature extraction for small defects and stabilizes initial cluster centers using K-Means++. Experimental results show that the modified YOLO model performs well in accuracy and detection speed.

ALGORITHMS (2021)

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Article Construction & Building Technology

Automatic detection of sewer defects based on improved you only look once algorithm

Yi Tan et al.

Summary: The paper proposed an improved CNN-based YOLOv3 method for automatic detection of sewage pipe defects, outperforming existing research on automatic detection efficiency.

AUTOMATION IN CONSTRUCTION (2021)

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Article Engineering, Multidisciplinary

Development of a YOLO-V3-based model for detecting defects on steel strip surface

Xupeng Kou et al.

Summary: A defect detection model based on YOLO-V3 was developed in this study, utilizing anchor-free feature selection mechanism and dense convolution blocks to improve feature reuse and characterization ability of the network, leading to higher performance compared to other models.

MEASUREMENT (2021)

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Article Materials Science, Textiles

An Improved Fabric Defect Detection Method Based on SSD

Huosheng Xie et al.

Summary: This study proposes an improved fabric defect detection algorithm based on SSD, which enhances detection accuracy by adding FCSE blocks and adjusting the number of default boxes. Experimental results confirm the effectiveness of this method in detecting various fabric defects.

AATCC JOURNAL OF RESEARCH (2021)

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Article Engineering, Electrical & Electronic