Accumulated CA–CFAR Process in 2-D for Online Object Detection From Sidescan Sonar Data

Abstract

This paper describes a novel approach to object detection from sidescan sonar (SSS) acoustical images. The current techniques of acoustical images processing consume a great deal of time and computational resources with many parameters to tune in order to obtain good quality images. This is due to the handling of the large data volume generated by these kinds of devices. The technique proposed in this work does not make any a priori assumption about the nature of the SSS image to be processed. However, it is able to make a segmentation of the image into two types of regions: acoustical highlight and seafloor reverberation areas, and based on this, it makes detection. The developed algorithm to achieve this consists of a migration and adaptation of a technique widely used in radar technology for detecting moving objects. This radar technique is known as the cell average-constant false alarm rate (CA-CFAR). This paper presents a drastic improvement of such approach by making an extension into 2-D analysis of the SSS image, in a way similar to integral image used in CA-CFAR detection for pulse Doppler radar. In this form, optimization of the computational effort is achieved. This new technique was called the accumulated cell average-constant false alarm rate in 2-D (ACA-CFAR 2-D). It was applied to pipeline detection and tracking with a very interesting degree of success. In addition, this technique provides similar results to image segmentation with respect to other frequently used approaches, but with much less computational resources and parameters to set. Its simplicity is a strong support of its robustness and accuracy. This feature makes it particularly attractive for using it in real-time applications, such as underwater robotics perception systems. This proposal was tested experimentally with acoustical data from SSS and the results detecting pipelines, and other shapes like sunken vessels or airplanes, are presented in this paper. Likewise, an experimental comparison with the results obtained with inverse undecimated discrete wavelet transform (UDWT) and active contours techniques is also presented.Index Terms-Cell average-constant false alarm rate (CA-CFAR), online object detection, sidescan sonar (SSS), sonar imagery.

Design and Implementation of a New CFAR Based on Weighted and Statistical Algorithms

“…A switching CFAR (S-CFAR), is presented in [10] to operate in a homogeneous environment and multiple targets with clutter transition situations. A 2D with optimize the computational time CFAR was proposed by [11]. A combination of cell-averaging and trimmedmean a proposed CATM-CFAR was presented by [12] to detect targets in Weibull clutter.…”

MACÁBOT: Prototipo de Vehículo Autónomo de Superficie (ASV)

“…Esta primera clasificación se realiza en este módulo mediante dos subsistemas, uno de seguimiento (Tracking Subsystem -TSS) y otro de detección de obstáculos (Obstacle Detection Subsystem -ODSS [12]). El TSS maneja los datos provenientes de ecosondas en el caso de realización de batimetrías, o de cualquier otro sensor de acuerdo a la aplicación, y debe ser capaz de realizar detecciones en tiempo eficiente [13][14]. El ODSS actual-mente emplea los datos provenientes de la ecosonda mecánicamente escaneada (Micron Tritech© de la Fig.…”

Acoustic Emission Burst Extraction for Multi-Level Leakage Detection in a Pipeline

“…To reduce the rate of error that occurs in the delayed time estimation and improve the localization accuracy of the algorithm, a critical issue existing in the AE fault detection system is to identify the leakage signal without providing false alarms. Previously, the constant fault alarm rate (CFAR) was used for object detection in the doppler radar system [24]. However, the modeling of bursts and impulses of AE signal in the role of objects which need to be detected by the CFAR, has not yet been studied.…”