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Wall segmentation in 2D images using convolutional neural networks.

Mihailo Bjekic¹, Ana Lazovic², Venkatachalam K³

¹Everseen, Belgrade, Serbia.

Peerj. Computer Science

|October 9, 2023

Summary

This summary is machine-generated.

This study introduces an effective module for wall segmentation in 2D images, achieving high accuracy. The proposed model offers faster execution and better performance than existing solutions for semantic segmentation tasks.

Keywords:

ADE20K Encoder-decoder PSPNet Semantic segmentation Wall segmentation

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Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Wall segmentation is a specialized area of semantic segmentation, crucial for identifying wall pixels within images.
Existing methods may lack efficiency or accuracy in distinguishing walls from other image elements.

Purpose of the Study:

To propose an effective module structure for semantic segmentation of walls in 2D images.
To enhance accuracy and execution speed compared to current wall segmentation techniques.

Main Methods:

Utilized an encoder-decoder architecture for the segmentation module.
Employed a Dilated ResNet50/101 network as the encoder, incorporating dilated convolutional layers.
Trained and evaluated models on a subset of the ADE20K dataset containing interior images.

Main Results:

The best performing model, using the proposed structure with ResNet101, achieved 92.13% pixel-level accuracy.
Achieved an Intersection over Union (IoU) of 72.58% on the validation dataset.
Demonstrated superior accuracy and faster execution compared to other evaluated solutions.

Conclusions:

The proposed module structure effectively addresses the challenges of wall segmentation in 2D images.
The developed model offers a robust and efficient solution for semantic segmentation tasks.
The approaches are adaptable for recognizing other objects, enabling diverse specific applications.