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A MaskFormer EfficientNet instance segmentation approach for crowd counting.

Silky Goel¹, Deepika Koundal²

¹School of Computer Science, UPES, Dehradun, Uttarakhand, 248007, India.

Scientific Reports

|April 17, 2025

Summary

This summary is machine-generated.

This study introduces a MaskFormer EfficientNetB7 Instance Segmentation model for accurate crowd counting. The novel approach effectively addresses challenges like scale and occlusion, outperforming existing methods in diverse scenarios.

Keywords:

Computer vision Convolutional neural network Crowd counting Instance segmentation Object detection Transformer

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Area of Science:

Computer Vision
Deep Learning

Background:

Object detection is a key challenge in computer vision.
Convolutional Neural Networks (CNNs) are widely used for object detection.
Crowd counting faces challenges such as scale variation, illumination changes, and occlusion.

Purpose of the Study:

To propose an efficient and accurate crowd counting model for challenging scenarios.
To leverage the MaskFormer architecture with EfficientNetB7 for improved performance.

Main Methods:

Utilized MaskFormer for mask classification and label prediction.
Employed EfficientNetB7 as a backbone for feature extraction.
Implemented a compound scaling algorithm for EfficientNetB7.

Main Results:

Achieved remarkable outcomes on UCF-QNRF, ShanghalTech (Part A and B), and Mall datasets.
Demonstrated superior performance compared to existing crowd counting approaches.
Showcased significant reductions in Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).

Conclusions:

The proposed MaskFormer EfficientNetB7 model offers high accuracy and efficiency in crowd counting.
The model exhibits strong generalizability across various environments and unseen data.
This approach provides a robust solution for complex crowd counting tasks.