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Large Span Sizes and Irregular Shapes Target Detection Methods Using Variable Convolution-Improved YOLOv8.

Yan Gao1, Wei Liu2, Hsiang-Chen Chui3

  • 1School of Intergated Circuits, Dalian University of Technology, Dalian 116024, China.

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Summary
This summary is machine-generated.

This study introduces an improved YOLOv8 object detection model to enhance accuracy and efficiency for irregular and small targets. The enhanced model achieves better performance in detecting challenging samples, crucial for real-time industrial inspection.

Keywords:
classificationimproved YOLOv8small objectsteel scrap

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Object detection models often struggle with irregularly shaped, small, or overlapping targets.
  • Existing methods face challenges with low-resolution labeling, background noise, and computational complexity.

Purpose of the Study:

  • To develop an improved YOLOv8 object detection method for enhanced accuracy and efficiency.
  • To address limitations in detecting spanning, irregularly shaped, and small targets.

Main Methods:

  • Incorporated a deformable convolution module into the YOLOv8 backbone to improve target perception.
  • Integrated the Sim-AM (simple parameter-free attention mechanism) module to enhance feature attention and reduce computational load.
  • Replaced spatial pyramid pooling with focal modulation networks to simplify the model structure and speed up detection.

Main Results:

  • The improved YOLOv8 model demonstrated a 2.1% increase in average precision (AP) and a 0.8% increase in mean average precision (mAP).
  • Achieved a reduction of 5.4 frames per second (FPS), indicating improved detection speed.
  • Experimental validation on a scrap steel dataset confirmed the model's effectiveness for diverse and challenging targets.

Conclusions:

  • The proposed variable convolution-improved YOLOv8 effectively enhances object detection accuracy and efficiency for complex industrial inspection tasks.
  • The integration of deformable convolution and Sim-AM modules, along with simplified network structure, offers a robust solution for real-time detection of irregular and small objects.