远程塔台由于其低成本高时效远程实时控制技术正越来越受到民航业界的青睐,其中运动目标自动检测和显示是远程塔台的核心技术,作为增强现实技术更好地为管制员提供服务。在分析远程塔台机场场面背景复杂、场面目标多为远场景、小目标等...远程塔台由于其低成本高时效远程实时控制技术正越来越受到民航业界的青睐,其中运动目标自动检测和显示是远程塔台的核心技术,作为增强现实技术更好地为管制员提供服务。在分析远程塔台机场场面背景复杂、场面目标多为远场景、小目标等特点基础上,提出了一种改进的You Only Look Once(YOLO)算法来实现远程塔台运动目标的检测,算法核心思想以Darknet-53为基础网络,多尺度预测边界框,以运动目标图像坐标的偏移量作为边框长宽的线性变换来实现边框的回归,改善了传统YOLO算法损失函数不同大小的边框未做区分的问题,提高了检测准确性和速度。机场真实数据实验表明,该算法能快速、准确的检测出远程塔台的运动目标,并准确的回归运动目标边框及分类。展开更多
For the detection of marine ship objects in radar images, large-scale networks based on deep learning are difficult to be deployed on existing radar-equipped devices. This paper proposes a lightweight convolutional ne...For the detection of marine ship objects in radar images, large-scale networks based on deep learning are difficult to be deployed on existing radar-equipped devices. This paper proposes a lightweight convolutional neural network, LiraNet, which combines the idea of dense connections, residual connections and group convolution, including stem blocks and extractor modules.The designed stem block uses a series of small convolutions to extract the input image features, and the extractor network adopts the designed two-way dense connection module, which further reduces the network operation complexity. Mounting LiraNet on the object detection framework Darknet, this paper proposes Lira-you only look once(Lira-YOLO), a lightweight model for ship detection in radar images, which can easily be deployed on the mobile devices. Lira-YOLO's prediction module uses a two-layer YOLO prediction layer and adds a residual module for better feature delivery. At the same time, in order to fully verify the performance of the model, mini-RD, a lightweight distance Doppler domain radar images dataset, is constructed. Experiments show that the network complexity of Lira-YOLO is low, being only 2.980 Bflops, and the parameter quantity is smaller, which is only 4.3 MB. The mean average precision(mAP) indicators on the mini-RD and SAR ship detection dataset(SSDD) reach 83.21% and 85.46%, respectively,which is comparable to the tiny-YOLOv3. Lira-YOLO has achieved a good detection accuracy with less memory and computational cost.展开更多
针对无人机航拍图像目标检测中视野变化大、时空信息复杂等问题,文中基于YOLOv5(You Only Look Once Version5)架构,提出基于图像低维特征融合的航拍小目标检测模型.引入CA(Coordinate Attention),改进MobileNetV3的反转残差块,增加图...针对无人机航拍图像目标检测中视野变化大、时空信息复杂等问题,文中基于YOLOv5(You Only Look Once Version5)架构,提出基于图像低维特征融合的航拍小目标检测模型.引入CA(Coordinate Attention),改进MobileNetV3的反转残差块,增加图像空间维度信息的同时降低模型参数量.改进YOLOv5特征金字塔网络结构,融合浅层网络中的特征图,增加模型对图像低维有效信息的表达能力,进而提升小目标检测精度.同时为了降低航拍图像中复杂背景带来的干扰,引入无参平均注意力模块,同时关注图像的空间注意力与通道注意力;引入VariFocal Loss,降低负样本在训练过程中的权重占比.在VisDrone数据集上的实验验证文中模型的有效性,该模型在有效提升检测精度的同时明显降低复杂度.展开更多
微动脉瘤是糖尿病视网膜病变的初期症状,消除该病灶可在早期非常有效地预防糖尿病视网膜病变。但由于视网膜结构复杂,同时眼底图像的成像由于患者、环境、采集设备等因素的不同会存在不同的亮度和对比度,现有的微动脉瘤检测算法难以实...微动脉瘤是糖尿病视网膜病变的初期症状,消除该病灶可在早期非常有效地预防糖尿病视网膜病变。但由于视网膜结构复杂,同时眼底图像的成像由于患者、环境、采集设备等因素的不同会存在不同的亮度和对比度,现有的微动脉瘤检测算法难以实现该病灶的精确检测和定位,为此本文提出嵌入SENet(squeeze-andexcitation networks)的改进YOLO(you only look once)v4自动检测算法。该算法在YOLOv4网络基础上,首先通过使用一种改进的快速模糊C均值聚类算法对目标样本进行先验框参数优化,以提高先验框与特征图的匹配度;然后,在主干网络嵌入SENet模块,通过强化关键信息,抑制背景信息,提高微动脉瘤的置信度;此外,还在网络颈部增加空间金字塔池化结构以增强主干网络输出特征的接受域,从而有助于分离出重要的上下文信息;最后,在Kaggle数据集上进行模型验证,并与其他方法进行对比。实验结果表明,与其他各种结构的YOLOv4网络模型相比,所提出的嵌入SENet的改进YOLOv4网络模型能显著提高检测结果(与原始YOLOv4相比Fscore提升了12.68%);与其他网络模型以及方法相比,所提出的嵌入SENet的改进YOLOv4网络模型的自动检测精度明显更优,且可实现精准定位。故本文所提出的嵌入SENet的改进YOLOv4算法性能较优,能准确、有效地检测并定位出眼底图像中的微动脉瘤。展开更多
文摘远程塔台由于其低成本高时效远程实时控制技术正越来越受到民航业界的青睐,其中运动目标自动检测和显示是远程塔台的核心技术,作为增强现实技术更好地为管制员提供服务。在分析远程塔台机场场面背景复杂、场面目标多为远场景、小目标等特点基础上,提出了一种改进的You Only Look Once(YOLO)算法来实现远程塔台运动目标的检测,算法核心思想以Darknet-53为基础网络,多尺度预测边界框,以运动目标图像坐标的偏移量作为边框长宽的线性变换来实现边框的回归,改善了传统YOLO算法损失函数不同大小的边框未做区分的问题,提高了检测准确性和速度。机场真实数据实验表明,该算法能快速、准确的检测出远程塔台的运动目标,并准确的回归运动目标边框及分类。
基金supported by the Joint Fund of Equipment Pre-Research and Aerospace Science and Industry (6141B07090102)。
文摘For the detection of marine ship objects in radar images, large-scale networks based on deep learning are difficult to be deployed on existing radar-equipped devices. This paper proposes a lightweight convolutional neural network, LiraNet, which combines the idea of dense connections, residual connections and group convolution, including stem blocks and extractor modules.The designed stem block uses a series of small convolutions to extract the input image features, and the extractor network adopts the designed two-way dense connection module, which further reduces the network operation complexity. Mounting LiraNet on the object detection framework Darknet, this paper proposes Lira-you only look once(Lira-YOLO), a lightweight model for ship detection in radar images, which can easily be deployed on the mobile devices. Lira-YOLO's prediction module uses a two-layer YOLO prediction layer and adds a residual module for better feature delivery. At the same time, in order to fully verify the performance of the model, mini-RD, a lightweight distance Doppler domain radar images dataset, is constructed. Experiments show that the network complexity of Lira-YOLO is low, being only 2.980 Bflops, and the parameter quantity is smaller, which is only 4.3 MB. The mean average precision(mAP) indicators on the mini-RD and SAR ship detection dataset(SSDD) reach 83.21% and 85.46%, respectively,which is comparable to the tiny-YOLOv3. Lira-YOLO has achieved a good detection accuracy with less memory and computational cost.
文摘针对无人机航拍图像目标检测中视野变化大、时空信息复杂等问题,文中基于YOLOv5(You Only Look Once Version5)架构,提出基于图像低维特征融合的航拍小目标检测模型.引入CA(Coordinate Attention),改进MobileNetV3的反转残差块,增加图像空间维度信息的同时降低模型参数量.改进YOLOv5特征金字塔网络结构,融合浅层网络中的特征图,增加模型对图像低维有效信息的表达能力,进而提升小目标检测精度.同时为了降低航拍图像中复杂背景带来的干扰,引入无参平均注意力模块,同时关注图像的空间注意力与通道注意力;引入VariFocal Loss,降低负样本在训练过程中的权重占比.在VisDrone数据集上的实验验证文中模型的有效性,该模型在有效提升检测精度的同时明显降低复杂度.
文摘微动脉瘤是糖尿病视网膜病变的初期症状,消除该病灶可在早期非常有效地预防糖尿病视网膜病变。但由于视网膜结构复杂,同时眼底图像的成像由于患者、环境、采集设备等因素的不同会存在不同的亮度和对比度,现有的微动脉瘤检测算法难以实现该病灶的精确检测和定位,为此本文提出嵌入SENet(squeeze-andexcitation networks)的改进YOLO(you only look once)v4自动检测算法。该算法在YOLOv4网络基础上,首先通过使用一种改进的快速模糊C均值聚类算法对目标样本进行先验框参数优化,以提高先验框与特征图的匹配度;然后,在主干网络嵌入SENet模块,通过强化关键信息,抑制背景信息,提高微动脉瘤的置信度;此外,还在网络颈部增加空间金字塔池化结构以增强主干网络输出特征的接受域,从而有助于分离出重要的上下文信息;最后,在Kaggle数据集上进行模型验证,并与其他方法进行对比。实验结果表明,与其他各种结构的YOLOv4网络模型相比,所提出的嵌入SENet的改进YOLOv4网络模型能显著提高检测结果(与原始YOLOv4相比Fscore提升了12.68%);与其他网络模型以及方法相比,所提出的嵌入SENet的改进YOLOv4网络模型的自动检测精度明显更优,且可实现精准定位。故本文所提出的嵌入SENet的改进YOLOv4算法性能较优,能准确、有效地检测并定位出眼底图像中的微动脉瘤。
