Authors

Abstract

Currently, research on cherry detection and recognition is relatively limited, and existing methods for agricultural product inspection often suffer from slow speed and low classification accuracy. To address these issues, this paper introduces an improved YOLOv11n-based model for detecting cherry ripeness, designed to enhance both the accuracy and efficiency of identifying cherries at different maturity stages. First, improvements were made to the backbone network of the YOLOv11n model by replacing the original backbone with ConvNeXtv2. This replacement achieved a broader global receptive field and enhanced multi-scale learning, which helped reduce computational costs and significantly improve efficiency while maintaining high performance. Second, a DCNv4 convolution module—an advanced convolutional layer with adaptive receptive fields—was added to the neck of the model. The neck is an intermediate stage that combines features from different layers, and the DCNv4 adapts the receptive field to help accurately locate occluded cherries of any shape and scale. This improves detection performance for small cherries without increasing computational complexity. Finally, the convolutional attention module CBAM was introduced. CBAM adaptively focuses on important image features while suppressing irrelevant background by using both channel and spatial attention mechanisms. Together, these additions significantly improve cherry detection accuracy and robustness. Our experimental results show that the improved M-YOLOv11n algorithm achieved a 4.84% increase in mAP@50 compared to the original YOLOv11n model. Precision and recall also improved by 1.25% and 0.4%, respectively. Overall, the enhanced model outperformed not only its base version but also the YOLOv5n and YOLOv8n models. Compared to multi-stage models, the proposed model demonstrates superior accuracy, speed, and reduced computational requirements. This improvement enables more efficient and precise identification of cherry ripeness, thereby enhancing the efficiency of cherry harvesting and facilitating optimal harvest timing. These advancements support the optimization of storage and transportation conditions for cherries and provide robust technical support for intelligent orchard management and the advancement of automated fruit sorting systems.

Abstract in Chinese

针对当前樱桃检测与识别研究较少，农产品检测与识别速度慢、分类精度低等问题，本文提出了一种基于改进YOLOv11n的樱桃成熟度检测模型，旨在提高不同成熟度的樱桃检测的准确性和效率。首先，针对YOLOv11n模型的主干网络进行了改进，将原有的主干网络替换为ConvNeXtv2，通过替换主干网络CSPDarknet11实现全局的感受野和多尺度学习，有助于降低计算成本，在保持高性能的同时，显著提高了计算效率。其次，在模型的颈部添加了DCNv4卷积模块，通过自适应地调整膨胀卷积的感受野，精准定位任意形状、任意尺度被遮挡的樱桃，在不增加额外计算量的同时改善小目标的检测效果。最后，引入卷积注意力模块CBAM，通过协同利用通道与空间注意力机制，自适应地聚焦关键特征并抑制背景干扰，从而显著提升模型对樱桃的检测精度与鲁棒性。实验结果表明，改进后的算法M-YOLOv11n相比原YOLOv11n模型mAP@50提高了4.84个百分点，精确率和召回率分别提高了1.25个百分点和0.4个百分点，均优于YOLOv5n、YOLOv8n和YOLOv11n模型。此外,与多阶段模型相比,该模型在平均精度、效率和计算负载方面均表现优越。由此可见,改进后的模型能够更加高效、精准地进行樱桃成熟度识别，这不仅提高了樱桃采摘效率，还精确控制了采摘时间，进而优化了果实的储存和运输条件，为果园智能化管理及水果自动分拣装备的开发提供了有效的技术支撑。