Retraining-Free Mixed-Precision Quantization for Power Equipment Defect Detection via Layer-Aware Particle Swarm Optimization
Year: 2025
Type: article
Abstract: Traditional deep learning methods have excelled in power equipment defect detection, but their high computational requirements hinder real deployment on edge devices. To address this challenge, this article introduced particle swarm optimization for retraining-free mixed-precision quantization (PSOQ), a novel post-training quantization framework enabling retraining-free mixed-precision quantization, through layer importance-guided particle swarm optimization (PSO) over a one-shot trained Supernet. Specifically, this framework constructed a Supernet using Monte Carlo sampling and interference-aware bit-width scheduling for fast, accurate evaluation of mixed-precision quantization configurations—without fine-tuning. Experiments on benchmark and insulator defect datasets demonstrated that PSOQ, when applied to various network architectures, significantly reduced computational and storage overhead, while maintaining detection accuracy.
DOI: 10.4018/ijsir.388948
Authors: Kena Chen, Lei Luo, Xuxu Li
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414511360
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