A Review on Deep Learning-based Methods for Template Matching: Advancing with QATM-Kalman-CNN Integration

Abstract

Introduction: Template matching algorithms have been widely used for identifying complex emissions in large datasets, offering a rapid initial assessment capability. However, their performance is often constrained by the requirement for prior training on target species and the high occurrence of false positives due to background noise interference. To enhance the efficiency and accuracy of these algorithms, we explore an improved approach leveraging deep learning techniques.

Objectives: This study aims to improve the robustness of template matching algorithms by integrating Quadrature Amplitude Time Modulation (QATM) as a preprocessing step for convolutional neural networks (CNNs). The primary goal is to optimize target species detection while mitigating false positives and enhancing performance in the presence of sophisticated interference.

Methods: The proposed method utilizes QATM to condition input data before feeding it into CNNs. CNNs, being one of the most fundamental and effective deep learning architectures, are employed for both training and prediction. By leveraging CNNs, we seek to automate the training process of target species detection within template matching algorithms, reducing dependence on manual intervention.

Results: Preliminary findings suggest that integrating QATM with CNNs enhances template matching performance, leading to a reduction in false positives and improved detection accuracy. The model demonstrates promising potential in handling complex signal interference, providing more reliable predictions.

Conclusions: This study highlights the feasibility of using CNNs in conjunction with QATM to enhance template matching performance. Future work will focus on refining this approach by conducting objective tests to fully automate target species training within template matching algorithms and further improve detection accuracy under challenging conditions.