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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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A parallel Canny edge detection algorithm based on OpenCL acceleration.

Yupu Song¹, Cailin Li², Shiyang Xiao³

¹College of Computer Engineering, Shangqiu Polytechnic, Shangqiu, China.

|January 5, 2024

Summary

This summary is machine-generated.

A new fine-grained parallel Canny edge detection method enhances real-time image processing. This OpenCL accelerated algorithm (OCL_Canny) offers significant speedups over CPU and CUDA implementations, proving effective and portable for big image data.

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Area of Science:

Computer Vision
Image Processing
Parallel Computing

Background:

Canny edge detection involves complex calculations, challenging real-time processing.
Traditional hardware acceleration (DSP, FPGA) has drawbacks like long development cycles and debugging difficulties.
Existing CUDA platforms lack cross-platform compatibility.

Purpose of the Study:

To develop a fine-grained parallel Canny edge detection method for improved efficiency.
To achieve CPU-GPU collaborative parallelism.
To address the limitations of existing Canny edge detection acceleration techniques.

Main Methods:

Optimized task partitioning for fine-grained parallelism.
Improved vector memory access patterns.
NDRange optimization for efficient parallel execution.
Implementation of CPU-GPU collaborative parallelism.
Design of parallel Canny edge detection on multi-core CPU and CUDA architectures.

Main Results:

The OpenCL accelerated Canny edge detection algorithm (OCL_Canny) achieved a 20.68x speedup over a CPU serial algorithm at 7452x8024 resolution.
OCL_Canny showed a 3.96x speedup compared to a CPU multi-threaded algorithm at 3500x3500 resolution.
A 1.21x speedup was observed for OCL_Canny against a CUDA-based algorithm at 1024x1024 resolution.

Conclusions:

The proposed parallel Canny edge detection algorithm demonstrates significant effectiveness and performance portability.
OCL_Canny provides a viable solution for accelerating image big data processing.
The study offers a valuable reference for fast image analysis in real-time applications.