Bridging the Gap between Laboratory and Field Experiments in American Eel Detection Using Transfer Learning and Convolutional Neural Network

An automatic system that utilizes data analytics and machine learning to identify adult American eel in data obtained by imaging sonars is created in this study. Wavelet transform has been applied to de-noise the ARIS sonar data and a convolutional neural network model has been built to classify eels and non-eel objects. Because of the unbalanced amounts of data in laboratory and field experiments, a transfer learning strategy is implemented to fine-tune the convolutional neural network model so that it performs well for both the laboratory and field data. The proposed system can provide important information to develop mitigation strategies for safe passage of out-migrating eels at hydroelectric facilities

Classification of Cylindrical Targets Buried in Seafloor Sediments

This paper presents the development and evaluation of a time-frequency processing technique for detection and classification of buried cylindrical targets from chirpbased parametric sonar data. The software is designed to discriminate between cylindrical targets —such as cables— of different diameters, which need to be identified as different from other strong reflectors or point targets. The method is evaluated on synthetic data generated with an acoustic scattering model for elastic cylinders for seven different diameters. The model generates characteristic responses of targets acquired by a parametric sonar system. The signal at the sonar receiver hydrophones is first windowed to reduce the data to the region of interest (buried target return). This return is then transformed using joint timefrequency transforms (we use the Wigner and Choi-Williams distributions) to produce a 2D image of the return. Dimensionality reduction and feature extraction are performed by singular value decomposition of this time-frequency image. Linear, quadratic, and Mahalanobis discriminant functions are then applied to the most significant singular values to produce the final classification. The study is carried out for various scenarios of free field response of targets as well as for responses from targets buried in sediment

Implementation of an Intelligent Target Classifier with Bicoherence Feature Set

ABSTRACT: This paper examines the feasibility of bispectral analysing of acoustic signals emanated from underwater targets, for the purpose of classification. Higher order analysis, especially bispectral analysis has been widely used to analyse signals when non-Gaussianity and non-linearity are involved. Bicoherence, which is a normalized form of bispectrum, has been used to extract source specific features, which is finally fed to a neural network classifier. Vector quantization has been used to reduce the dimensionality of the feature set, thereby reducing computational costs. Simulations were carried out with linear, tan and log-sigmoid transfer functions and also with different code book sizes. It is found that the bicoherence feature set can provide acceptable levels of classification accuracy with a properly trained neural network classifier

Comparison of different classification algorithms for underwater target discrimination

Includes bibliographical references.Classification of underwater targets from the acoustic backscattered signals is considered here. Several different classification algorithms are tested and benchmarked not only for their performance but also to gain insight to the properties of the feature space. Results on a wideband 80-kHz acoustic backscattered data set collected for six different objects are presented in terms of the receiver operating characteristic (ROC) and robustness of the classifiers wrt reverberation.This work was supported by the Office of Naval Research, Biosonar Program, under Grant N00014-99-1-0166 and Grant N00014-01-1-0307. Data and technical support were provided by the NSWC, Coastal Systems Station, Panama City, FL

Genetic Programming for Object Detection : a Two-Phase Approach with an Improved Fitness Function

This paper describes two innovations that improve the efficiency and effectiveness of a genetic programming approach to object detection problems. The approach uses genetic programming to construct object detection programs that are applied, in a moving window fashion, to the large images to locate the objects of interest. The first innovation is to break the GP search into two phases with the first phase applied to a selected subset of the training data, and a simplified fitness function. The second phase is initialised with the programs from the first phase, and uses the full set of training data with a complete fitness function to construct the final detection programs. The second innovation is to add a program size component to the fitness function. This approach is examined and compared with a neural network approach on three object detection problems of increasing difficulty. The results suggest that the innovations increase both the effectiveness and the efficiency of the genetic programming search, and also that the genetic programming approach outperforms a neural network approach for the most difficult data set in terms of the object detection accuracy

Integrated optimization of underwater acoustic ship-radiated noise recognition based on two-dimensional feature fusion

Abstract(#br)Feature fusion methods are introduced to ship-radiated noise recognition in this paper. Wavelet packet (WP) decomposition is used to decompose the ship-radiated noise into multiple different subbands. By considering the features extracted from the different subbands reflecting different characteristics of the ship-radiated noise, a two-dimensional feature fusion (2DFF) scheme is proposed to fuse the features extracted from the different subbands. Principal component analysis (PCA) and canonical correlation analysis (CCA) are used in the 2DFF scheme. Then, a so-called discriminative ability improving (DAI) strategy is proposed to improve the discriminative ability of the extracted features. Starting at the 2DFF, a processing chain of feature fusion and ship-radiated noise recognition is designed and jointly optimized to the task. The 2DFF scheme and DAI strategy are tested on real ship-radiated noise data recorded. Experimental results indicate that compared with the baseline, the 2DFF scheme can improve 7.25% of recognition accuracy. Experimental results also show that the DAI strategy can further improve the recognition accuracy of 13.10%