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Liquid Reservoir Weld Defect Detection Based on Improved YOLOv8s.

Zonghang Li1, Tao Song1, Bin Zhou1

  • 1College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China.

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Summary
This summary is machine-generated.

This study introduces an enhanced YOLOv8s algorithm for detecting weld defects in automotive liquid reservoirs. The improved model significantly boosts detection accuracy for various defect types, enhancing quality control in manufacturing.

Keywords:
YOLOv8sdefect detectionliquid reservoir welds

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Area of Science:

  • Mechanical Engineering
  • Computer Vision
  • Materials Science

Background:

  • Weld seam defects on automotive liquid reservoirs pose quality control challenges.
  • Traditional detection methods are insufficient for varied defect shapes and scales.

Purpose of the Study:

  • To develop an advanced algorithm for accurate detection of liquid reservoir weld defects.
  • To improve the performance of the YOLOv8s model for detecting defects with size variations and complex features.

Main Methods:

  • Proposed an optimized YOLOv8s algorithm incorporating Reparameterized Generalized Feature Pyramid Network (RepGFPN) and a small-object detection head.
  • Replaced Spatial Pyramid Pooling Fast (SPPF) with Focal Modulation Networks (FocalNets) for complex defect identification.
  • Integrated Cascaded Group Attention (CGA) mechanism to reduce redundant feature information propagation.

Main Results:

  • Achieved a 6.3% improvement in mAP@0.5 and 4.3% in mAP@0.5:0.95 over the original YOLOv8s model.
  • Demonstrated significant AP improvements for craters (3.9%), porosity (13.5%), undercuts (5.0%), and lack of fusion (2.5%).
  • Outperformed state-of-the-art models on both liquid reservoir and steel pipe weld defect datasets.

Conclusions:

  • The enhanced YOLOv8s algorithm provides superior performance for detecting diverse weld defects in automotive components.
  • The proposed optimizations effectively address challenges related to defect size variation and complexity.
  • This method offers a robust solution for automated quality inspection in manufacturing.