农业装备与车辆工程2026，Vol.64Issue(1)：36-42,48,8.DOI:10.3969/j.issn.1673-3142.2026.01.005
基于改进ShuffleNet V2的板栗智能分级方法研究
Research on intelligent grading method of chestnuts based on improved ShuffleNet V2
陈倩娥 1焦洪磊1
作者信息
- 1. 河北科技师范学院数学与信息科技学院,河北 秦皇岛 066004
- 折叠
摘要
Abstract
To achieve accurate detection of appearance defects in Chinese chestnuts,the existing ShuffleNet V2 network was improved.By introducing the Convolutional Block Attention Module(CBAM),the model's ability to extract subtle defect features such as mold spots and insect holes was enhanced.Meanwhile,the Focal Loss function was adopted to alleviate the training bias caused by the imbalance between positive and negative samples.Experimental results showed that the CBAM module effectively improves the model's sensitivity to identifying defect regions,and Focal Loss strengthens the focus on hard-to-classify samples,increasing the recall rate of defect-level Chinese chestnut recognition from 85.6%to 91.8%.The proposed model maintains high classification accuracy while possessing excellent lightweight characteristics,with a parameter count of only 3.6 M and an inference time of 3.2 ms per image.关键词
板栗等级划分/缺陷检测/ShuffleNet V2/注意力模块Key words
classification of chestnuts grades/defect detection/ShuffleNet V2/attention module分类
轻工纺织引用本文复制引用
陈倩娥,焦洪磊..基于改进ShuffleNet V2的板栗智能分级方法研究[J].农业装备与车辆工程,2026,64(1):36-42,48,8.
