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Distance Measurements by Taping01:18

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Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...
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Real-Time Tracking of Object Melting Based on Enhanced DeepLab v3+ Network.

Tian-Yu Jiang1,2,3,4, Feng-Lan Ju5, Ya-Xun Dai5

  • 1Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan, Hebei 063210, China.

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Summary
This summary is machine-generated.

This study introduces an improved DeepLab v3+ network for real-time tracking and segmentation of molten silicon dioxide (SiO2) particles in iron tailings. The enhanced model significantly improves accuracy and efficiency for high-temperature material analysis.

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Area of Science:

  • Materials Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Iron tailings dissolution in blast furnace slag is crucial for material processing.
  • Tracking molten silicon dioxide (SiO2) particles at high temperatures presents challenges due to information loss and inaccurate extraction.
  • Existing methods struggle with real-time segmentation of small, high-temperature particles.

Purpose of the Study:

  • To develop a robust method for real-time tracking, segmentation, and extraction of molten SiO2 particles.
  • To address the limitations of current techniques in handling high-temperature particle analysis.
  • To improve the accuracy and efficiency of small object detection in challenging environments.

Main Methods:

  • An improved DeepLab v3+ network architecture was proposed, featuring dense atrous spatial pyramid pooling (ASPP) modules for optimized feature extraction.
  • Shallow convolutional layers of the backbone network were enhanced and merged into the decoding part for detailed feature capture.
  • Integration of the lightweight MobileNet v3 network reduced parameters and accelerated image detection for real-time segmentation.
  • A hybrid loss function combining Dice Loss and Focal Loss was developed to handle the imbalance of small positive samples in the dataset.

Main Results:

  • The proposed model achieved a 6% higher mean intersection over union (MIoU) for small object segmentation compared to the original model.
  • Overall MIoU increased by 3%, demonstrating improved segmentation performance.
  • Execution time and memory consumption were reduced by half, enabling real-time processing.
  • The model effectively addressed the challenges of unbalanced datasets and small object detection.

Conclusions:

  • The improved DeepLab v3+ network provides an effective solution for real-time tracking and segmentation of small particles in high-temperature environments.
  • The method enhances accuracy and efficiency in analyzing molten SiO2 particles within iron tailings and blast furnace slag.
  • This approach is well-suited for applications requiring low-level hardware configurations and real-time material analysis.