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Study on the Evaluation Method of Sound Phase Cloud Maps Based on an Improved YOLOv4 Algorithm.

Qinfeng Zhu¹, Huifeng Zheng¹, Yuebing Wang¹

¹Key Laboratory of Acoustics Research, China Jiliang University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)

|August 5, 2020

Summary

This study introduces a new evaluation method for sound imaging instruments using an improved YOLOv4 algorithm to precisely assess sound source localization. The method enhances accuracy and speed in determining positioning errors for better instrument performance analysis.

Keywords:

DenseNet YOLOv4 algorithm k-medians++positioning error sound imaging instrument sound source localization

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Echo Particle Image Velocimetry

Echo Particle Image Velocimetry

Published on: December 27, 2012

Area of Science:

Acoustics and Optics
Machine Learning for Signal Processing
Instrumentation and Measurement

Background:

Quantitative data from sound imaging instruments lacks a unified evaluation method for combined acoustic and optical images.
Objective and comprehensive evaluation of sound source localization is crucial for instrument performance.
Current methods do not adequately address the comprehensive evaluation of combined acoustic and optical imaging results.

Purpose of the Study:

To define a localization error index for sound imaging instruments.
To propose an acoustic phase cloud map evaluation method using an improved YOLOv4 algorithm.
To enable direct and objective evaluation of sound source localization results.

Main Methods:

Image augmentation of acoustic phase cloud maps to create a training dataset.
Integration of DenseNet with clustering algorithms to enhance the YOLOv4 algorithm for feature extraction.
Utilizing a trained neural network for pixel-level localization error determination.
Employing a chessboard grid to correlate map size with physical distance for true positioning error calculation.

Main Results:

The improved YOLOv4 algorithm achieved a mean average precision of 96.3% and an F1-score of 95.2% in acoustic phase cloud map detection.
Detection speed reached up to 34.6 frames per second.
The method accurately determines pixel-level localization errors and true lateral and longitudinal positioning errors.

Conclusions:

The proposed evaluation method rapidly and accurately determines the positioning error of sound imaging instruments.
This approach provides a robust tool for analyzing and evaluating the positioning performance of sound imaging instruments.
The improved YOLOv4 algorithm offers significant advancements in objective sound source localization evaluation.