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ASW-YOLO: Lightweight Gear Defect Detection with Improved YOLOv8n.

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Title: ASW-YOLO: Lightweight Gear Defect Detection with Improved YOLOv8n.
Authors: Luo, Zhecheng1 (AUTHOR), Zheng, Bin1 (AUTHOR) zhengbin@pzhu.edu.cn
Source: Materials (1996-1944). Jun2026, Vol. 19 Issue 11, p2309. 29p.
Subjects: Object recognition (Computer vision), Machine learning, Quality control, Industrial applications
Abstract: Aiming at the problems of diverse defect types, large-scale differences, and complex background interference in gear surface defect detection, a lightweight model, ASW-YOLO, is proposed based on YOLOv8n. By using an ADown dual downsampling module to compress feature map resolution and preserve fine-grained information. C2f_SE channel attention is introduced to enhance small-scale defect response. The CIoU is replaced with WIoU to optimize multi-scale target localization accuracy. The experiments are conducted on the gear dataset. The comparative experiments show that mAP@0.5 of ASW-YOLO reached 94.8%, an increase of 4.5% compared to YOLOv8n, with a reduction of 9.3% in parameter count and 8.5% in computational complexity. The ablation experiments confirm the effectiveness of the three modules. ASW-YOLO achieves a 4.5% increase in mAP@0.5 and a 6.1% increase in recall compared to YOLOv8n. The generalization experiments demonstrate that the mAP@0.5 fluctuation of ASW-YOLO remains below 2% under strong highlight and striped shadow. Moreover, the model maintains over 85% mAP@0.5 under motion blur. ASW-YOLO balances precision and lightweight, making it suitable for real-time quality monitoring in industry. [ABSTRACT FROM AUTHOR]
Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: ASW-YOLO: Lightweight Gear Defect Detection with Improved YOLOv8n.
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  Data: <searchLink fieldCode="JN" term="%22Materials+%281996-1944%29%22">Materials (1996-1944)</searchLink>. Jun2026, Vol. 19 Issue 11, p2309. 29p.
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  Data: <searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+control%22">Quality control</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+applications%22">Industrial applications</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Aiming at the problems of diverse defect types, large-scale differences, and complex background interference in gear surface defect detection, a lightweight model, ASW-YOLO, is proposed based on YOLOv8n. By using an ADown dual downsampling module to compress feature map resolution and preserve fine-grained information. C2f_SE channel attention is introduced to enhance small-scale defect response. The CIoU is replaced with WIoU to optimize multi-scale target localization accuracy. The experiments are conducted on the gear dataset. The comparative experiments show that mAP@0.5 of ASW-YOLO reached 94.8%, an increase of 4.5% compared to YOLOv8n, with a reduction of 9.3% in parameter count and 8.5% in computational complexity. The ablation experiments confirm the effectiveness of the three modules. ASW-YOLO achieves a 4.5% increase in mAP@0.5 and a 6.1% increase in recall compared to YOLOv8n. The generalization experiments demonstrate that the mAP@0.5 fluctuation of ASW-YOLO remains below 2% under strong highlight and striped shadow. Moreover, the model maintains over 85% mAP@0.5 under motion blur. ASW-YOLO balances precision and lightweight, making it suitable for real-time quality monitoring in industry. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Materials (1996-1944) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/ma19112309
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      – Code: eng
        Text: English
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        PageCount: 29
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      – SubjectFull: Object recognition (Computer vision)
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Quality control
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      – SubjectFull: Industrial applications
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      – TitleFull: ASW-YOLO: Lightweight Gear Defect Detection with Improved YOLOv8n.
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            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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              Value: 19
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