Addressing class imbalance in deep learning for acoustic target classification

Abstract

Acoustic surveys provide important data for fisheries management. During the surveys, ship-mounted echo sounders send acoustic signals into the water and measure the strength of the reflection, so-called backscatter. Acoustic target classification (ATC) aims to identify backscatter signals by categorizing them into specific groups, e.g. sandeel, mackerel, and background (as bottom and plankton). Convolutional neural networks typically perform well for ATC but fail in cases where the background class is similar to the foreground class. In this study, we discuss how to address the challenge of class imbalance in the sampling of training and validation data for deep convolutional neural networks. The proposed strategy seeks to equally sample areas containing all different classes while prioritizing background data that have similar characteristics to the foreground class. We investigate the performance of the proposed sampling methodology for ATC using a previously published deep convolutional neural network architecture on sandeel data. Our results demonstrate that utilizing this approach enables accurate target classification even when dealing with imbalanced data. This is particularly relevant for pixel-wise semantic segmentation tasks conducted on extensive datasets. The proposed methodology utilizes state-of-the-art deep learning techniques and ensures a systematic approach to data balancing, avoiding ad hoc methods.

Fcg-Former: Identification of Functional Groups in FTIR Spectra Using Enhanced Transformer-Based Model

“…Spectra were then segmented into seven groups to address this imbalance. This method ensures that models are trained and evaluated on data sets accurately representing the distribution of functional groups, thereby promoting a more balanced data set regarding the number of functional groups per molecule . Subsequently, all groups were randomly partitioned into training, validation, and test sets with allocation ratios of 75, 15, and 10%, respectively.…”

“…This method ensures that models are trained and evaluated on data sets accurately representing the distribution of functional groups, thereby promoting a more balanced data set regarding the number of functional groups per molecule. 51 Subsequently, all groups were randomly partitioned into training, validation, and test sets with allocation ratios of 75, 15, and 10%, respectively. During the training process, input spectra values were scaled to a range of 0 to 1 using the min− max normalization method, ensuring consistent data scales.…”