华南农业大学学报2025，Vol.46Issue(3)：419-428,10.DOI:10.7671/j.issn.1001-411X.202407012
基于BiFPN和Triplet注意力机制的YOLOv5s缺陷苹果识别算法
A YOLOv5s algorithm based on BiFPN and Triplet attention mechanism for identifing defective apple
摘要
Abstract
[Objective]In order to make full use of context information and integrate multi-scale features,a YOLOv5s algorithm based on BiFPN and Triplet attention mechanism(BTF-YOLOv5s)for identifing defective apple was proposed.[Method]Firstly,the additional weights were introduced to the weighted bidirectional feature pyramid network(BiFPN)to learn the importance of different input features.The model realized the repeated fusion of multi-scale features through the top-down and bottom-up bidirectional paths,and improved the multi-scale detection ability.Secondly,the Triplet attention mechanism was applied to the Neck layer to enhance the model's ability to represent the correlation between target and contextual information,so that the model could focus more on the learning of apple features.Finally,the Focal-CIoU loss function was used to adjust the loss weight,so that the model payed more attention to defective apple recognition,and improved the perception ability of the model.Different loss functions were compared through ablation experiments.The position of attention mechanism in YOLOv5 structure was changed,and compared with the mainstream algorithms.[Result]On the basis of the initial YOLOv5s model,BTF-YOLOv5s improved the accuracy,recall and mAP by 5.7,2.2 and 3.5 percentage points respectively,and the memory usage of the model was 14.7 MB.The average accuracy of BTF-YOLOv5s was 5.7,3.5,13.3,3.5,2.9,2.6,2.8 and 0.3 percentage points higher than those of SSD,YOLOv3,YOLOv4,YOLOv5s,YOLOv7,YOLOv8n,YOLOv8s and YOLOv9,respectively.[Conclusion]The model of BTF-YOLOv5s shows significant superiority in identifing defective apples,which provides certain technical support for the picking robot to realize the automatic sorting of high-quality apples and defective apples in the picking process.关键词
YOLOv5s/缺陷苹果/注意力机制/损失函数/目标检测/采摘机器人Key words
YOLOv5s/Defective apple/Attention mechanism/Loss function/Object detection/Picking robot分类
信息技术与安全科学引用本文复制引用
惠永永,赵春雨,宋昭漾,赵小强..基于BiFPN和Triplet注意力机制的YOLOv5s缺陷苹果识别算法[J].华南农业大学学报,2025,46(3):419-428,10.基金项目
国家自然科学基金(62263021) （62263021）
兰州市青年科技人才创新项目(2023-QN-36) （2023-QN-36）
