Effective Parallelization Method for Object Recognition in 2D Sonar Images Based on Task Partitioning

Techniques for analyzing and avoiding hazardous objects and situations on the seabed are being developed to ensure the safety of ships and submersibles from various hazards. Improvements in accuracy and real-time response are critical for underwater object recognition, which rely on underwater sonar detection to remove noises and analyze the data. Therefore, parallel processing is being introduced for real-time processing of two-dimensional (2D) underwater sonar detector images for seabed monitoring. However, this requires optimized parallel processing between the modules for image processing and the data processing of a vast amount of data. This study proposes an effective parallel processing method, called Task Partitioning, based on central and graphical processing units for monitoring and identifying underwater objects in real time based on 2D-imaging sonar. The practicality of the proposed method is evaluated experimentally by comparing it to the sequential processing method. The experimental results show that the Task Partitioning method significantly improves the processing time for sonar images because it reduces the average execution time to 1% and 5% of the sequential processing method and general parallelization, respectively.

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RT-Seg: A Real-Time Semantic Segmentation Network for Side-Scan Sonar Images

Sensors ◽

10.3390/s19091985 ◽

2019 ◽

Vol 19 (9) ◽

pp. 1985

Author(s):

Qi Wang ◽

Meihan Wu ◽

Fei Yu ◽

Chen Feng ◽

Kaige Li ◽

...

Keyword(s):

High Resolution ◽

Real Time ◽

Autonomous Underwater Vehicle ◽

Semantic Segmentation ◽

Processing Unit ◽

Real Time Processing ◽

Side Scan Sonar ◽

Network Training ◽

Sonar Sensor ◽

Sonar Images

Real-time processing of high-resolution sonar images is of great significance for the autonomy and intelligence of autonomous underwater vehicle (AUV) in complex marine environments. In this paper, we propose a real-time semantic segmentation network termed RT-Seg for Side-Scan Sonar (SSS) images. The proposed architecture is based on a novel encoder-decoder structure, in which the encoder blocks utilized Depth-Wise Separable Convolution and a 2-way branch for improving performance, and a corresponding decoder network is implemented to restore the details of the targets, followed by a pixel-wise classification layer. Moreover, we use patch-wise strategy for splitting the high-resolution image into local patches and applying them to network training. The well-trained model is used for testing high-resolution SSS images produced by sonar sensor in an onboard Graphic Processing Unit (GPU). The experimental results show that RT-Seg can greatly reduce the number of parameters and floating point operations compared to other networks. It runs at 25.67 frames per second on an NVIDIA Jetson AGX Xavier on 500*500 inputs with excellent segmentation result. Further insights on the speed and accuracy trade-off are discussed in this paper.

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A real-time processing method for microwave staring correlated imaging based on sequential LS

2016 CIE International Conference on Radar (RADAR) ◽

10.1109/radar.2016.8059498 ◽

2016 ◽

Author(s):

Bo Liu ◽

Chao Tian ◽

Jianlin Zhang ◽

Dongjin Wang

Keyword(s):

Real Time ◽

Processing Method ◽

Real Time Processing ◽

Time Processing

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A Real-Time Processing Method of Underwater Ambient Noise Spectrum Based on One-Third Octave

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.333-335.522 ◽

2013 ◽

Vol 333-335 ◽

pp. 522-525

Author(s):

Chao Ma ◽

Chun Jie Qiao ◽

Yue Ke Wang ◽

Shen Zhao

Keyword(s):

Real Time ◽

Ambient Noise ◽

Noise Spectrum ◽

Processing Method ◽

Real Time Processing ◽

Time Processing ◽

Object A ◽

Passive Detection ◽

Continuous Measuring ◽

Underwater Object

Extended, continuous measuring and analysis for underwater ambient noise can find the objects like ships and submarine. It is important for passive detection of underwater object. A real-time processing method of one-third octave spectrum is presented. It is shown by tests that the method is steady and satisfy the real-time processing.

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A Kind of Dual-Channel GMTI Real-Time Processing Method Based on Frequency DPCA

2006 CIE International Conference on Radar ◽

10.1109/icr.2006.343552 ◽

2006 ◽

Author(s):

Longmei Xi ◽

Changyao Zhang

Keyword(s):

Real Time ◽

Processing Method ◽

Real Time Processing ◽

Time Processing ◽

Dual Channel

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Real-time processing method of telemetry position controlling instructions with fuzzy set theory

2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) ◽

10.1109/fskd.2011.6019656 ◽

2011 ◽

Author(s):

Guo-bao Wang ◽

Jiu-ming Lv ◽

Hong Chen ◽

Rui-ming Jia

Keyword(s):

Real Time ◽

Set Theory ◽

Fuzzy Set ◽

Fuzzy Set Theory ◽

Processing Method ◽

Real Time Processing ◽

Time Processing

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Implementation of Parallel Processing on Multi-Object Recognition System Software

Lontar Komputer Jurnal Ilmiah Teknologi Informasi ◽

10.24843/lkjiti.2018.v09.i03.p02 ◽

2018 ◽

pp. 124

Author(s):

Midriem Mirdanies

Keyword(s):

Object Recognition ◽

Parallel Processing ◽

Processing Time ◽

Application Programming Interface ◽

Recognition System ◽

Scale Invariant ◽

Development Environment ◽

Sequential Processing ◽

Speeded Up Robust Features ◽

Recognition Software

Multi-object recognition software on Remote Controlled Weapon Station (RCWS) had been implemented in previous paper using Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) methods, but the processing time in one cycle is quite slow so it is need to be optimized using parallel processing. In this paper, implementation of parallel processing on multi-object recognition software has been done on a multicore processor. The Openmp Application Programming Interface (API), C programming language, and Visual studio Integrated Development Environment (IDE) is used to implement the parallel processing in this paper. The parallel processing was implemented in the for loop of the matching process between the capturing object from the camera and the database under two conditions, i.e., the original of the for loop syntax and after optimization of the for loop syntax. Experiments have been done on the core processor i7-4790 @ 3.60Ghz, 8 GB DDR3 of memory, windows 8.1 os using two, four, six, and eight cores to recognize one, two, three and four objects at once using SIFT and SURF methods. Based on the experiments, it was found that the processing time in parallel is faster than sequential process, where the fastest of the processing time is obtained after optimization in the loop syntax, with the processing time in recognizing one to four objects using SIFT method is 927.13 ms (8 core), 1019.31 ms (6 core), 1190.72 ms (8 core), and 1283.05 ms (4 core), where the sequential processing time in recognizing one to four objects is 1067.35 ms, 1164.78 ms, 1352.93 ms, and 1497.35 ms, while the processing time in recognizing one to four objects using SURF method is 1157.13 ms (8 core), 1517.83 ms (6 core), 1572.14 ms (4 core), dan 1472.64 ms (6 core), where the sequential processing time in recognizing one to four objects is 5635.99 ms, 6268.47 ms, 3256.63 ms, dan 3883.78 ms.

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