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Optimization Algorithm of Moving Object Detection Using Multiscale Pyramid Convolutional Neural Networks.

Zhe Yang^1,2, Ziyu Bu^1,2, Yexin Pan^1,2

¹School of Computer Science and Technology, Soochow University, Suzhou 215006, China.

Computational Intelligence and Neuroscience

|March 21, 2023

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This study introduces an optimized convolutional neural network (CNN) model for enhanced moving target identification. The novel approach significantly improves detection accuracy and positioning information for various targets.

Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Object detection and recognition are critical research areas.
Existing methods face challenges with insufficient positioning information and low detection accuracy.

Purpose of the Study:

To develop an optimized model for moving target identification using convolutional neural networks (CNNs).
To enhance target detection accuracy and improve positioning information.

Main Methods:

Fusing target detection and depth semantic segmentation models to acquire classification and semantic location information.
Utilizing multiscale image features and a pyramid structure for robust detection of various target sizes and shapes.
Employing CNN migration and context information learning for improved feature extraction and scene adaptability.

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Main Results:

Achieved an accuracy rate of 0.941, outperforming the LSTM-NMS algorithm by 0.189.
Demonstrated enhanced robustness and scene adaptability in feature extraction.
Significantly improved the accuracy of moving target position detection.

Conclusions:

The proposed CNN-based model offers a significant advancement in moving target identification.
The fusion of detection and segmentation models, along with multiscale features, leads to superior performance.
The method shows great potential for real-world applications requiring accurate object detection.