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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.

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This article describes the core algorithms of the perception system to be included within an autonomous underwater vehicle (AUV). This perception system is based on the acoustic data acquired from side scan sonar (SSS). These data should be processed in an efficient time, so that the perception system is able to detect and recognize a predefined target. This detection and recognition outcome is therefore an important piece of knowledge for the AUVs dynamic mission planner (DMP). Effectively, the DMP should propose different trajectories, navigation depths and other parameters that will change the robot's behaviour according to the perception system output. Hence, the time in which to make a decision is critical in order to assure safe robot operation and to acquire good quality data; consequently, the efficiency of the on-line image processing from acoustic data is a key issue.Current techniques for acoustic data processing are time and computationally intensive. Hence, it was decided to process data coming from a SSS using a technique that is used for radars, due to its efficiency and its amenability to on-line processing. The engineering problem to solve in this case was underwater pipeline tracking for routine inspections in the off-shore industry. Then, an automatic oil pipeline detection system was developed borrowing techniques from the processing of radar measurements. The radar technique is known as Cell Average -Constant False Alarm Rate (CA -CFAR). With a slight variation of the algorithms underlying this radar technique, which consisted of the previous accumulation of partial sums, a great improvement in computing time and effort was achieved. Finally, a comparison with previous approaches over images acquired with a SSS from a vessel in the Salvador de Bahia bay in Brazil showed the feasibility of using this on-board technique for AUV perception.