Improved DCFE-YOLOv8 algorithm.

<div><p>Accurate detection of fabric defects is crucial for quality control in the textile industry. However, the task of fabric defect detection remains highly challenging due to the complex textures and diverse defect patterns. To address the issues of inaccurate localization and false...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Lei Zhou (19808) (author)
مؤلفون آخرون:	Bingya Ma (20558620) (author), Yanyan Dong (1304145) (author), Zhewen Yin (11198799) (author), Fan Lu (490738) (author)
منشور في:	2025
الموضوعات:	Biochemistry Cell Biology Sociology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified varying defect sizes reducing computational complexity false positives caused experimental results demonstrate dynamic snake convolution diverse defect patterns precise defect localization div >< p detecting complex defects 3 %, highlighting inaccurate localization complex textures subtle defects 5 %, texture details textile industry superior capability spatial scales significant improvement scale structures scale attention quality control paper proposes model ’ leveraging multi improved yolov8 feature representations fabric defects enhance sensitivity efficient multi detection performance channel priors backbone network accurate detection 95 rose
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Improved DCFE-YOLOv8 algorithm.

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