PLUM

Accurate recognition of targets in the orchard environment is the key to vision perception for picking robots. Factors such as small, densely growing plum fruit targets and high occlusion lead to unsatisfactory recognition of plum fruit by vision algorithms. Therefore, this paper proposes an improved YOLOv5s model to detect highly occluded and dense plums in orchards. First, the backbone network of YOLOv5s is improved in this paper. A new structure Focus-Maxpool module is used to replace the downsampling convolution in the backbone network, so that the model can retain more feature information of highly occluded targets and small targets when downsampling, thus improving the detection performance of occluded targets and small targets. Second, the loss function is improved in this paper. The weighted loss of focal loss and cross entropy function is used as the classification loss of the model to reduce the interference of noise on focal loss and improve the recognition ability of the model for adhering targets. Finally, several testing experiments were designed to evaluate the model's performance. The results show that the improved YOLOv5s model has better average precision than YOLOv5s, YOLOv4, Faster-RCNN, SSD, and Centernet. Compared with the results of the YOLOv5s model, the improved model's average precision, recall, and accuracy are improved by 2.84%, 9.53%, and 1.66%, respectively, compared with the original model. Moreover, the detection speed of the improved model can reach 91.37 frames/s, which can meet the demand for real-time detection. The results show that the improved detection model has high accuracy and robustness in natural orchard environments, which can provide data reference for the research of picking robots and the work of orchard environment monitoring.

果园环境下的目标精准识别是采摘机器人视觉感知的核心关键。受李子果实目标体积小巧、生长密集且遮挡程度较高等因素影响，现有视觉算法对李子果实的识别效果往往不尽如人意。为此，本文提出一种改进型YOLOv5s模型，用于检测果园内高遮挡、高密度分布的李子果实。首先，本文对YOLOv5s的主干网络进行改进：采用全新的Focus-Maxpool模块替换主干网络中的下采样卷积，使模型在执行下采样操作时能够保留更多高遮挡目标与小目标的特征信息，进而提升对遮挡目标及小目标的检测性能。其次，本文对损失函数进行优化：以焦点损失（focal loss）与交叉熵函数（cross entropy function）的加权损失作为模型的分类损失，以降低噪声对焦点损失的干扰，增强模型对粘连目标的识别能力。最后，本文设计多组测试实验对所提模型的性能展开评估。实验结果表明，改进后的YOLOv5s模型的平均精度（average precision）优于YOLOv5s、YOLOv4、Faster-RCNN、SSD以及Centernet等主流算法。相较于原始YOLOv5s模型，改进后模型的平均精度、召回率（recall）与准确率（accuracy）分别提升2.84%、9.53%与1.66%；且改进模型的检测速度可达91.37帧/s，可满足实时检测的实际需求。综合来看，该改进型检测模型在自然果园环境中具备较高的精度与鲁棒性，可为采摘机器人的相关研究以及果园环境监测工作提供可靠的数据参考。

搜集汇总

背景与挑战

背景概述

PLUM数据集是一个用于果园环境中李子目标识别的计算机视觉数据集，旨在解决高遮挡和密集生长的小目标检测问题。它基于改进的YOLOv5s模型，通过优化网络结构和损失函数，提升了平均精度、召回率和检测速度，适用于采摘机器人和果园环境监测研究。