传感技术学报2024，Vol.37Issue(2)：303-309,7.DOI:10.3969/j.issn.1004-1699.2024.02.015
基于改进YOLOv4-Tiny的机械零件目标检测算法
Recognition of Mechanical Parts Based on Improved YOLOv4-Tiny Algorithm
摘要
Abstract
In order to improve the problems of large detection error and low accuracy in traditional mechanical parts feature extraction algorithm,common mechanical parts are taken as the research target and lightweight network in deep learning algorithm is adopted as the base model for optimization.CSP-Darknet53 is used to extract the feature.An improved MA-RFB module is added after the feature extraction network,and multi-branch convolution and empty convolution was introduced to strengthen the receptive field.In addition,the neck network is improved,PANet is selected to replace FPN,and the attention module of CBAM is added to form RC-PANet for multi-scale detection of parts targets.AP reaches 96.47％in the self-made part dataset,and the detection speed is 0.001 38 s per sample.Without losing too much speed,compared with the original YOLOv4-Tiny network,AP improves by 2.80％,and the improved algorithm achieves a balance in speed and precision,which reflects the theoretical and application value of the research.关键词
图像分类/轻量级网络/深度学习/机械零件/感受野Key words
image classification/lightweight network/deep learning/mechanical parts/receptive field module分类
信息技术与安全科学引用本文复制引用
杜保帅,房桐,赵燕成,赵景波..基于改进YOLOv4-Tiny的机械零件目标检测算法[J].传感技术学报,2024,37(2):303-309,7.基金项目
国家自然科学基金项目(51475251) （51475251）
山东省自然科学基金项目(ZR2013FM014) （ZR2013FM014）
山东省高等学校科技计划项目(J12LN37) （J12LN37）
青岛市科技计划项目(15-9-2-109-nsh) （15-9-2-109-nsh）
青岛市民生计划项目(22-3-7-xdny-18-nsh) （22-3-7-xdny-18-nsh）
