现有无人机跟踪方法存在对远距离无人机检测精度较低、参数量大难以实时跟踪、目标易丢失的问题。因此,提出一种基于改进YOLOv8(you only look once version 8)的轻量级无人机跟踪方法。针对现有方法对远距离无人机检测精度较低的问题,...现有无人机跟踪方法存在对远距离无人机检测精度较低、参数量大难以实时跟踪、目标易丢失的问题。因此,提出一种基于改进YOLOv8(you only look once version 8)的轻量级无人机跟踪方法。针对现有方法对远距离无人机检测精度较低的问题,以YOLOv8为基线模型,替换网络结构中原始卷积模块为空间到深度分组的卷积,在降低网络参数的基础上提高模型对小目标的特征提取能力。针对模型参数量大导致模型难以实时跟踪的问题,设计一种深度可分离混洗网络结构作为模型主干网络,在保证检测精度的同时缩减模型参数量。针对普通跟踪模型跟踪易丢失的问题,结合改进检测模型与ByteTrack算法提高对复杂环境下无人机的跟踪性能。在Real World数据集上对跟踪方法进行验证,相较基线模型,改进无人机检测模型的检测精度提高1.6%,召回率提高0.8%,F1度量值提高0.2,平均检测精度提高0.5%,参数量减小0.2×10^(6),证明模型有较好的检测精度和实时性。对无人机飞行视频进行跟踪测试,结果表明所提方法对无人机跟踪有较好的性能。展开更多
目的利用2023年新推出的YOLOv8m网络,开发一款人工智能辅助系统,旨在实现腺瘤性息肉的自动定位和诊断。方法使用4个结肠息肉数据集,总计包括9411张静态图像和25段视频。所涵盖的息肉类别包括增生性息肉和腺瘤性息肉。利用LabelMe工具对...目的利用2023年新推出的YOLOv8m网络,开发一款人工智能辅助系统,旨在实现腺瘤性息肉的自动定位和诊断。方法使用4个结肠息肉数据集,总计包括9411张静态图像和25段视频。所涵盖的息肉类别包括增生性息肉和腺瘤性息肉。利用LabelMe工具对图像进行标注,并将标注数据转换成适用于深度学习模型训练的YOLO格式。在模型训练方面,采用预训练的YOLOv5m和YOLOv8m模型,并结合实时数据增强以及多种图像处理技术进行迁移学习训练。模型性能的评估采用多个指标,包括敏感性、特异性、假阳性率和检测速度(每秒帧数,frames per second,FPS)、平均精度(mean average precision,mAP)等。此外,还使用混淆矩阵进行详细评估,并将模型的性能与不同资历的医师进行比较分析。结果在对1411个息肉的验证集进行评估中,YOLOv8m模型在多项性能指标上超越了YOLOv5。YOLOv8m的整体准确率为98.58%,在腺瘤性息肉、增生性息肉检测的敏感性分别为98.06%和99.32%,特异性分别为99.33%和98.09%,不同类型息肉预测的mAP50为0.994。在与内镜医师的性能比较中,YOLOv8m模型在准确率(98.58%)和处理速度(60.61帧/s)方面均优于低年资(准确率为86.02%)和高年资内镜医师(准确率为93.14%),其处理速度是低年资内镜医师的67.2倍。结论基于YOLOv8m网络的深度学习模型能够快速、精确地检测与分类结直肠息肉,在辅助内镜医师提高腺瘤性息肉检出率方面展现出很大的应用潜力。展开更多
Target detection is an important task in computer vision research, and such an anomaly detection and the topic of small target detection task is more concerned. However, there are still some problems in this kind of r...Target detection is an important task in computer vision research, and such an anomaly detection and the topic of small target detection task is more concerned. However, there are still some problems in this kind of researches, such as small target detection in complex environments is susceptible to background interference and poor detection results. To solve these issues, this study proposes a method which introduces the attention mechanism into the you only look once(YOLO) network. In addition, the amateur-produced mask dataset was created and experiments were conducted. The results showed that the detection effect of the proposed mothed is much better.展开更多
文摘现有无人机跟踪方法存在对远距离无人机检测精度较低、参数量大难以实时跟踪、目标易丢失的问题。因此,提出一种基于改进YOLOv8(you only look once version 8)的轻量级无人机跟踪方法。针对现有方法对远距离无人机检测精度较低的问题,以YOLOv8为基线模型,替换网络结构中原始卷积模块为空间到深度分组的卷积,在降低网络参数的基础上提高模型对小目标的特征提取能力。针对模型参数量大导致模型难以实时跟踪的问题,设计一种深度可分离混洗网络结构作为模型主干网络,在保证检测精度的同时缩减模型参数量。针对普通跟踪模型跟踪易丢失的问题,结合改进检测模型与ByteTrack算法提高对复杂环境下无人机的跟踪性能。在Real World数据集上对跟踪方法进行验证,相较基线模型,改进无人机检测模型的检测精度提高1.6%,召回率提高0.8%,F1度量值提高0.2,平均检测精度提高0.5%,参数量减小0.2×10^(6),证明模型有较好的检测精度和实时性。对无人机飞行视频进行跟踪测试,结果表明所提方法对无人机跟踪有较好的性能。
文摘目的利用2023年新推出的YOLOv8m网络,开发一款人工智能辅助系统,旨在实现腺瘤性息肉的自动定位和诊断。方法使用4个结肠息肉数据集,总计包括9411张静态图像和25段视频。所涵盖的息肉类别包括增生性息肉和腺瘤性息肉。利用LabelMe工具对图像进行标注,并将标注数据转换成适用于深度学习模型训练的YOLO格式。在模型训练方面,采用预训练的YOLOv5m和YOLOv8m模型,并结合实时数据增强以及多种图像处理技术进行迁移学习训练。模型性能的评估采用多个指标,包括敏感性、特异性、假阳性率和检测速度(每秒帧数,frames per second,FPS)、平均精度(mean average precision,mAP)等。此外,还使用混淆矩阵进行详细评估,并将模型的性能与不同资历的医师进行比较分析。结果在对1411个息肉的验证集进行评估中,YOLOv8m模型在多项性能指标上超越了YOLOv5。YOLOv8m的整体准确率为98.58%,在腺瘤性息肉、增生性息肉检测的敏感性分别为98.06%和99.32%,特异性分别为99.33%和98.09%,不同类型息肉预测的mAP50为0.994。在与内镜医师的性能比较中,YOLOv8m模型在准确率(98.58%)和处理速度(60.61帧/s)方面均优于低年资(准确率为86.02%)和高年资内镜医师(准确率为93.14%),其处理速度是低年资内镜医师的67.2倍。结论基于YOLOv8m网络的深度学习模型能够快速、精确地检测与分类结直肠息肉,在辅助内镜医师提高腺瘤性息肉检出率方面展现出很大的应用潜力。
基金supported by the National Key Research and Development Program of China (No.2022YFE0196000)the National Natural Science Foundation of China (No.61502429)。
文摘Target detection is an important task in computer vision research, and such an anomaly detection and the topic of small target detection task is more concerned. However, there are still some problems in this kind of researches, such as small target detection in complex environments is susceptible to background interference and poor detection results. To solve these issues, this study proposes a method which introduces the attention mechanism into the you only look once(YOLO) network. In addition, the amateur-produced mask dataset was created and experiments were conducted. The results showed that the detection effect of the proposed mothed is much better.
