Performance Benchmarking and Optimization of Deep Learning-Based Point Cloud Processing Algorithms for Industrial Quality Inspection

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Published: 2026-03-31
Keywords:
point cloud processing, deep learning, performance benchmarking, Industrial quality inspection

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Yuhan Li
Computer Science, Northeastern University, Boston, MA, USA

Abstract

The deployment of deep learning-based point cloud processing algorithms in industrial quality inspection systems requires comprehensive performance evaluation frameworks to guide algorithm selection and optimization. This study establishes a systematic benchmarking methodology that evaluates state-of-the-art point cloud neural networks across multiple dimensions, including recognition accuracy, computational efficiency, and robustness under industrial conditions. Through controlled experiments on synthetic and real-world manufacturing datasets, we quantify the performance tradeoffs among competing approaches and identify optimal configuration strategies for different deployment scenarios. Our benchmark framework incorporates standardized metrics for accuracy assessment, efficiency profiling, and robustness evaluation. The empirical findings provide data-driven guidelines for manufacturing enterprises to select appropriate point cloud processing solutions.

Article Details

Issue

Vol. 2 No. 1 (2026)

Section

Articles

How to Cite

Performance Benchmarking and Optimization of Deep Learning-Based Point Cloud Processing Algorithms for Industrial Quality Inspection. (2026). Journal of Science, Innovation & Social Impact, 2(1), 328-340. https://sagespress.com/index.php/JSISI/article/view/114

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