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Related Concept Videos

Extraction: Advanced Methods00:56

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
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Segregation in fresh concrete is a phenomenon where the components of the concrete mix separate, leading to uneven distribution and compromised structural integrity. This separation typically occurs when concrete is subjected to excessive horizontal movement within forms, or when it is dropped from considerable heights or forced through narrow, winding paths. As a result, heavier coarse aggregate particles settle at the bottom, while lighter, finer materials such as cement and water rise to the...
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The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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An industrial carbon block instance segmentation algorithm based on improved YOLOv8.

Runjie Shi1, Zhengbao Li2, Zewei Wu1

  • 1College of Ocean Science and Engineering, Shandong University of Science and Technology, No 579, Qian Wan Gang Road, Qingdao, 266590, Qing Dao, China.

Scientific Reports
|March 10, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces YOLOv8-HDSA, an improved instance segmentation algorithm for industrial carbon block recognition. It enhances accuracy in identifying carbon block types and segmenting edges, crucial for intelligent manufacturing applications.

Keywords:
Carbon block instance segmentationIndustrial automationMachine visionReinforce feature fusionYOLO

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Industrial Automation

Background:

  • Machine vision is key for industrial intelligent applications, particularly in automated carbon block cleaning.
  • Accurate carbon block type recognition and center point localization are critical but challenging for existing instance segmentation algorithms.

Purpose of the Study:

  • To develop an improved instance segmentation algorithm (YOLOv8-HDSA) for accurate industrial carbon block recognition and edge segmentation.
  • To enhance feature representation and fusion capabilities for better performance in industrial settings.

Main Methods:

  • Proposed YOLOv8-HDSA algorithm featuring a Selective Reinforcement Feature Fusion Module (SRFF) using Hadamard product and dilated convolution.
  • Incorporated a convolutional self-attention mechanism with residual structure in the head for improved feature extraction.
  • Introduced Focaler-IoU as the loss function to optimize regression performance.

Main Results:

  • YOLOv8-HDSA demonstrated significant improvements on real industrial datasets.
  • Achieved a 7.2% increase in average carbon block recognition accuracy.
  • Improved carbon block edge segmentation accuracy by 3.8%.

Conclusions:

  • The proposed YOLOv8-HDSA algorithm effectively addresses limitations of existing methods for industrial carbon block instance segmentation.
  • The enhancements in feature fusion, attention mechanisms, and loss function contribute to superior recognition and segmentation accuracy.