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基于STM32微控制器的表面缺陷视觉检测方法

来源：电工电气发布时间：2024-03-11 08:11 浏览次数：58

基于STM32微控制器的表面缺陷视觉检测方法

汪国平¹，胡博²，陈仲生^1，3，侯幸林³

(1 湖南工业大学电气与信息工程学院，湖南株洲 412007；

2 南京理工大学瞬态物理国家重点实验室，江苏南京 210094；

3 常州工学院汽车工程学院, 江苏常州 213032)

摘要：表面缺陷检测是产品质检的重要工序之一，现有深度学习视觉检测大多基于云端服务器，存在模型大、算力需求高、成本高等不足。以 STM32 微控制器为核心，提出了一种基于轻量化网络的表面缺陷视觉检测方法，采用轻量级 SSD 作为缺陷检测模型，利用 MobileNetV1 替换原有的骨干网络 VGG-16 以减小网络规模；采用 INT8 量化的训练后量化方法对模型进行计算加速，生成的 TFlite 模型仅有 578 KB，运行占用 RAM 仅为 288.29 KB，并在 STM32 微控制器中实现了模型的移植和部署。实验测试结果表明，该方法能实现锂电池表面划痕和凹坑两种缺陷的边缘侧准确检测。

关键词： 表面缺陷检测；轻量化网络；视觉检测；STM32 微控制器

中图分类号：TM930.12+6 ；TM930.9 文献标识码：A 文章编号：1007-3175(2024)02-0047-06

Visual Detection Method of Surface Defects Based on STM32 Microcontroller

WANG Guo-ping¹, HU Bo², CHEN Zhong-sheng^1,3, HOU Xing-lin³

（1 College of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China;

2 National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, China;

3 College of Automotive Engineering, Changzhou Institute of Technology, Changzhou 213032, China）

Abstract: Surface defect detection is one of important processes of product quality inspection, most of existing deep learning visual inspection is based on cloud servers, which has disadvantages of large models, high requirements of computing power and high costs. To this end, this paper uses the STM32 microcontroller as the core and proposes an light-weight network-based visual detection method of surface defects. Firstly, the lightweight SSD is used as the defect detection model, where the original backbone network VGG-16 is replaced by the MobileNetv1 to reduce the network scale. Then, the post-training quantization method based on the INT8 quantization is used to accelerate the model calculation, and the generated TFlite model was only 578 KB, and the RAM occupied by operation was only 288.29 KB, and the model was ported and deployed in the STM32 microcontroller. Finally, the experimental test results show that the proposed method can accurately detect the edge side of scratches and pits on the surface of lithium batteries.

Key words: surface defect detection; light-weight network; visual detection; STM32 microcontroller

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基于STM32微控制器的表面缺陷视觉检测方法