近年来,以ChatGPT为代表的能够适应复杂场景、并能满足人类的各种应用需求为目标的文本生成算法模型成为学术界与产业界共同关注的焦点.然而,ChatGPT等大规模语言模型(Large Language Model,LLM)高度忠实于用户意图的优势隐含了部分的...近年来,以ChatGPT为代表的能够适应复杂场景、并能满足人类的各种应用需求为目标的文本生成算法模型成为学术界与产业界共同关注的焦点.然而,ChatGPT等大规模语言模型(Large Language Model,LLM)高度忠实于用户意图的优势隐含了部分的事实性错误,而且也需要依靠提示内容来控制细致的生成质量和领域适应性,因此,研究以内在质量约束为核心的文本生成方法仍具有重要意义.本文在近年来关键的内容生成模型和技术对比研究的基础上,定义了基于内在质量约束的文本生成的基本形式,以及基于“信、达、雅”的6种质量特征;针对这6种质量特征,分析并总结了生成器模型的设计和相关算法;同时,围绕不同的内在质量特征总结了多种自动评价和人工评价指标与方法.最后,本文对文本内在质量约束技术的未来研究方向进行了展望.展开更多
随着传统农业逐渐向智慧农业转型,室温条件下具有低成本、长寿命、低功耗、小型化的检测手段对于农业环境及动植物本体指标检测至关重要,尤其对于无法进行电路有线连接的农业场景。随着器件传感和无线通信的整合,无芯片射频识别(chiples...随着传统农业逐渐向智慧农业转型,室温条件下具有低成本、长寿命、低功耗、小型化的检测手段对于农业环境及动植物本体指标检测至关重要,尤其对于无法进行电路有线连接的农业场景。随着器件传感和无线通信的整合,无芯片射频识别(chipless radio frequency identification,CRFID)因为具有轻量、价格合理、普适性等优势被广泛应用,CRFID标签具有理论上的“无限”寿命,代替了集成电路,成为标识传感信息融合的重要媒介,适用于农业环境、动植物生长监测、物流运输、食品品质检测等。该研究首先介绍了CRFID系统构成,以及其基本原理,指出天线是CRFID实现跨域感知的关键要素之一,随着农业场景中气体、环境温湿度、pH值等变化,天线负载敏感材料的电导率、磁导率、介电常数变化,引起CRFID标签的反向散射信号变化;其次,基于时频域标签,介绍了CRFID用于湿度、温度、气体(二氧化碳、氨气、乙烯)、pH和食品(猪肉、牛肉、鱼肉、果蔬、牛奶)检测的国内外最新研究进展,对比了相关传感器的关键性能指标;最后,针对CRFID技术的成功案例,指出了该类型传感器面临的主要研究挑战、未来研究方向和潜在解决方案,指出了未来智慧农业检测场景的低功耗、小型化、抗干扰发展趋势。CRFID技术的成功应用将有利于提升农业场景传感检测的智慧化程度,有助于人类及动植物生产活动健康评估。展开更多
This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy ...This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is 90.95%,the mAP value is 91.35%,and the detection time of each image is 21 ms,which can still meet the requirements of real-time detection of tomatoes in dense planting environment.The experimental results show that the proposed TDA-YOLO model can accurately and quickly detect tomatoes in dense planting environment,and at the same time avoid the use of a large number of annotated data,which provides technical support for the development of automatic harvesting systems for tomatoes and other fruits.展开更多
文摘近年来,以ChatGPT为代表的能够适应复杂场景、并能满足人类的各种应用需求为目标的文本生成算法模型成为学术界与产业界共同关注的焦点.然而,ChatGPT等大规模语言模型(Large Language Model,LLM)高度忠实于用户意图的优势隐含了部分的事实性错误,而且也需要依靠提示内容来控制细致的生成质量和领域适应性,因此,研究以内在质量约束为核心的文本生成方法仍具有重要意义.本文在近年来关键的内容生成模型和技术对比研究的基础上,定义了基于内在质量约束的文本生成的基本形式,以及基于“信、达、雅”的6种质量特征;针对这6种质量特征,分析并总结了生成器模型的设计和相关算法;同时,围绕不同的内在质量特征总结了多种自动评价和人工评价指标与方法.最后,本文对文本内在质量约束技术的未来研究方向进行了展望.
文摘随着传统农业逐渐向智慧农业转型,室温条件下具有低成本、长寿命、低功耗、小型化的检测手段对于农业环境及动植物本体指标检测至关重要,尤其对于无法进行电路有线连接的农业场景。随着器件传感和无线通信的整合,无芯片射频识别(chipless radio frequency identification,CRFID)因为具有轻量、价格合理、普适性等优势被广泛应用,CRFID标签具有理论上的“无限”寿命,代替了集成电路,成为标识传感信息融合的重要媒介,适用于农业环境、动植物生长监测、物流运输、食品品质检测等。该研究首先介绍了CRFID系统构成,以及其基本原理,指出天线是CRFID实现跨域感知的关键要素之一,随着农业场景中气体、环境温湿度、pH值等变化,天线负载敏感材料的电导率、磁导率、介电常数变化,引起CRFID标签的反向散射信号变化;其次,基于时频域标签,介绍了CRFID用于湿度、温度、气体(二氧化碳、氨气、乙烯)、pH和食品(猪肉、牛肉、鱼肉、果蔬、牛奶)检测的国内外最新研究进展,对比了相关传感器的关键性能指标;最后,针对CRFID技术的成功案例,指出了该类型传感器面临的主要研究挑战、未来研究方向和潜在解决方案,指出了未来智慧农业检测场景的低功耗、小型化、抗干扰发展趋势。CRFID技术的成功应用将有利于提升农业场景传感检测的智慧化程度,有助于人类及动植物生产活动健康评估。
基金The National Natural Science Foundation of China (32371993)The Natural Science Research Key Project of Anhui Provincial University(2022AH040125&2023AH040135)The Key Research and Development Plan of Anhui Province (202204c06020022&2023n06020057)。
文摘This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is 90.95%,the mAP value is 91.35%,and the detection time of each image is 21 ms,which can still meet the requirements of real-time detection of tomatoes in dense planting environment.The experimental results show that the proposed TDA-YOLO model can accurately and quickly detect tomatoes in dense planting environment,and at the same time avoid the use of a large number of annotated data,which provides technical support for the development of automatic harvesting systems for tomatoes and other fruits.
