FCOS-LSC: A Novel Model for Green Fruit Detection in a Complex Orchard Environment.

IF 7.6 1区 农林科学 Q1 AGRONOMY
Ruina Zhao, Yujie Guan, Yuqi Lu, Ze Ji, Xiang Yin, Weikuan Jia
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引用次数: 0

Abstract

To better address the difficulties in designing green fruit recognition techniques in machine vision systems, a new fruit detection model is proposed. This model is an optimization of the FCOS (full convolution one-stage object detection) algorithm, incorporating LSC (level scales, spaces, channels) attention blocks in the network structure, and named FCOS-LSC. The method achieves efficient recognition and localization of green fruit images affected by overlapping occlusions, lighting conditions, and capture angles. Specifically, the improved feature extraction network ResNet50 with added deformable convolution is used to fully extract green fruit feature information. The feature pyramid network (FPN) is employed to fully fuse low-level detail information and high-level semantic information in a cross-connected and top-down connected way. Next, the attention mechanisms are added to each of the 3 dimensions of scale, space (including the height and width of the feature map), and channel of the generated multiscale feature map to improve the feature perception capability of the network. Finally, the classification and regression subnetworks of the model are applied to predict the fruit category and bounding box. In the classification branch, a new positive and negative sample selection strategy is applied to better distinguish supervised signals by designing weights in the loss function to achieve more accurate fruit detection. The proposed FCOS-LSC model has 38.65M parameters, 38.72G floating point operations, and mean average precision of 63.0% and 75.2% for detecting green apples and green persimmons, respectively. In summary, FCOS-LSC outperforms the state-of-the-art models in terms of precision and complexity to meet the accurate and efficient requirements of green fruit recognition using intelligent agricultural equipment. Correspondingly, FCOS-LSC can be used to improve the robustness and generalization of the green fruit detection models.

Abstract Image

Abstract Image

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FCOS-LSC:复杂果园环境下青果检测的新模型。
为了更好地解决机器视觉系统中绿果识别技术设计的困难,提出了一种新的水果检测模型。该模型是对FCOS (fully convolution one-stage object detection)算法的优化,在网络结构中加入了LSC (level scales, space, channel)关注块,命名为FCOS-LSC。该方法对受重叠遮挡、光照条件和捕获角度影响的青果图像实现了高效的识别和定位。具体来说,利用改进后的特征提取网络ResNet50加入可变形卷积,充分提取青果特征信息。采用特征金字塔网络(FPN)以交叉连接和自顶向下连接的方式充分融合底层细节信息和高层语义信息。接下来,在生成的多尺度特征图的尺度、空间(包括特征图的高度和宽度)和通道三个维度上分别添加注意机制,提高网络的特征感知能力。最后,应用该模型的分类和回归子网络对水果类别和边界框进行预测。在分类分支中,采用新的正负样本选择策略,通过在损失函数中设计权值,更好地区分监督信号,实现更准确的水果检测。提出的FCOS-LSC模型参数为38.65M,浮点运算为38.72G,青苹果和青柿子的平均检测精度分别为63.0%和75.2%。综上所述,FCOS-LSC在精度和复杂度上都优于目前最先进的模型,能够满足利用智能农业设备进行青果识别的准确和高效需求。相应的,FCOS-LSC可以提高青果检测模型的鲁棒性和泛化性。
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来源期刊
Plant Phenomics
Plant Phenomics Multiple-
CiteScore
8.60
自引率
9.20%
发文量
26
审稿时长
14 weeks
期刊介绍: Plant Phenomics is an Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and published by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. The mission of Plant Phenomics is to publish novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics. The scope of the journal covers the latest technologies in plant phenotyping for data acquisition, data management, data interpretation, modeling, and their practical applications for crop cultivation, plant breeding, forestry, horticulture, ecology, and other plant-related domains.
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