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Mask R-CNN-Oriented Pottery Display and Identification System.

¹Hubei Academy of Fine Arts, Wuhan, Hubei 430205, China.

Computational Intelligence and Neuroscience

|June 23, 2022

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This study introduces an automated pottery identification system using Mask R-CNN, improving accuracy for pottery classification and decoration recognition. The novel method achieves over 90% comprehensive accuracy, outperforming traditional techniques.

Area of Science:

Archaeological Science
Computer Vision
Materials Science

Background:

Traditional pottery identification is labor-intensive and expensive.
Existing methods lack efficiency for industrial-scale applications.
Automated systems are needed to meet modern demands.

Purpose of the Study:

To develop an automated pottery identification system using Mask R-CNN.
To enhance the accuracy and efficiency of pottery classification.
To address the limitations of small datasets in pottery recognition.

Main Methods:

Implemented a Mask R-CNN framework with a generalized intersection over union (GIoU) loss function.
Utilized transfer learning to overcome data scarcity.
Applied mask and minimum bounding box algorithms for precise feature extraction of pottery contours and decorations.

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Main Results:

Achieved comprehensive pottery recognition accuracy exceeding 90%.
Demonstrated superior accuracy in identifying pottery color decoration (above 90%) and grain decoration (above 87%).
The proposed method significantly outperformed existing pottery recognition techniques.

Conclusions:

The Mask R-CNN-based system offers a highly accurate and efficient solution for automated pottery identification.
The enhanced feature extraction and loss function contribute to superior performance, especially for detailed decorations.
This approach has significant implications for the pottery industry, archaeology, and cultural heritage preservation.