食品与机械2024，Vol.40Issue(1)：115-121,7.DOI:10.13652/j.spjx.1003.5788.2023.80432
基于YOLO-FFD的水果品种和新鲜度识别方法
Fruit variety and freshness recognition method based on YOLO-FFD
摘要
Abstract
Objective:In order to improve the situation that existing fruit recognition and classification methods rely on manual operation and complex equipment.Methods:A lightweight model YOLO-FFD(YOLO with fruit and freshen detection)was proposed,which based on the YOLOv5 framework.Firstly,LightweightC3 was designed as the basic unit of the backbone feature extraction network based on the depth separable convolution and GELU activation function,which reduced the number of model parameters and computation,and speeds up the convergence of the model.Secondly,EnhancedC3,a large kernel depth separable convolution module,was used to improve the neck of the original model.suppressed information loss and enhance the feature fusion ability of the model,so as to improve the detection accuracy of the model.Finally,GSConv was used to replace the common convolution in the feature fusion network to further lighten the model.Results:The experimental results showed that the average accuracy of the proposed model reached 96.12％,the FPS on RTX 3090 was 172,and the speed on the embedded Jetson TX2 was 20 frames per second.Compared with the original YOLOv5 model,the mAP was improved by 2.21％,the calculation amount was reduced by 26％,and the speed was increased by two times.Conclusion:YOLO-FFD can meet the requirement of identifying fruit varieties and freshness,and improve the falsely detection and missing detection in complex scenes.关键词
水果/新鲜度/品种识别/轻量化/深度学习/目标检测Key words
fruit/freshness/variety identification/lightweight/deep learning/object detection引用本文复制引用
鄢紫,陈良艳,刘卫华,赖华清,叶胜..基于YOLO-FFD的水果品种和新鲜度识别方法[J].食品与机械,2024,40(1):115-121,7.基金项目
湖北省高校优秀中青年科技创新团队项目(编号:T2021009) （编号:T2021009）
