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

Abstract

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.

A Fast and Effective Image Preprocessing Method for Hot Round Steel Surface

“…With CUDA, GPU can be more easily used for general purpose computing [27]. Many literature studies have proved that using CUDA computing in image processing can improve processing efficiency [28][29][30][31][32][33][34][35]. For example, Zhan et al proposed a fast CUDA-based image preprocessing method which includes image graying, Gaussian filtering, histogram equalization, and other processes of image preprocessing and achieved high-speed parallel processing.…”