Aiming at the problems of low detection accuracy and large model size of existing object detection algorithms applied to complex road scenes,an improved you only look once version 8(YOLOv8)object detection algorithm f...Aiming at the problems of low detection accuracy and large model size of existing object detection algorithms applied to complex road scenes,an improved you only look once version 8(YOLOv8)object detection algorithm for infrared images,F-YOLOv8,is proposed.First,a spatial-to-depth network replaces the traditional backbone network's strided convolution or pooling layer.At the same time,it combines with the channel attention mechanism so that the neural network focuses on the channels with large weight values to better extract low-resolution image feature information;then an improved feature pyramid network of lightweight bidirectional feature pyramid network(L-BiFPN)is proposed,which can efficiently fuse features of different scales.In addition,a loss function of insertion of union based on the minimum point distance(MPDIoU)is introduced for bounding box regression,which obtains faster convergence speed and more accurate regression results.Experimental results on the FLIR dataset show that the improved algorithm can accurately detect infrared road targets in real time with 3%and 2.2%enhancement in mean average precision at 50%IoU(mAP50)and mean average precision at 50%—95%IoU(mAP50-95),respectively,and 38.1%,37.3%and 16.9%reduction in the number of model parameters,the model weight,and floating-point operations per second(FLOPs),respectively.To further demonstrate the detection capability of the improved algorithm,it is tested on the public dataset PASCAL VOC,and the results show that F-YOLO has excellent generalized detection performance.展开更多
主流道路车辆目标检测算法在复杂环境下对小目标识别精度低,易因遮挡和定位不准确造成漏检、误检。提出了改进版YOLOv5算法。针对道路上的小目标,改进Head检测层结构,添加大尺度目标检测层,提高道路上小目标检测精度。为适应目标的形状...主流道路车辆目标检测算法在复杂环境下对小目标识别精度低,易因遮挡和定位不准确造成漏检、误检。提出了改进版YOLOv5算法。针对道路上的小目标,改进Head检测层结构,添加大尺度目标检测层,提高道路上小目标检测精度。为适应目标的形状和尺度变化多样,在颈部网络引入全维动态卷积(Omni-Dimensional Dynamic Convolution,ODConv),对原卷积模块进行替换,提高特征提取能力。为了充分利用全局信息,在颈部网络引入全局注意力机制(Global Attention Mechanism,GAM),提升特征提取能力。针对定位精度问题,引入MPDIoU损失函数,使预测框与真实框更加符合。实验结果表明,改进的YOLOv5算法在自动驾驶数据集KITTI上平均精度均值(mean Average Precision,mAP)达到88.7%,相较于基准模型提高了2%,每秒帧数(Frames per Second,FPS)提升了12%。改进算法的检测精度更高,检测速度更快,有效改善了复杂道路条件下的目标检测问题。展开更多
非结构化道路的缺陷目标检测任务对道路交通安全具有重要意义,但检测所需的标注数据集相对有限。为了解决非结构化道路标注数据集缺乏以及现有模型对无标注数据学习能力不足的问题,提出一种MAM(Multi-Augmentation with Memory)半监督...非结构化道路的缺陷目标检测任务对道路交通安全具有重要意义,但检测所需的标注数据集相对有限。为了解决非结构化道路标注数据集缺乏以及现有模型对无标注数据学习能力不足的问题,提出一种MAM(Multi-Augmentation with Memory)半监督目标检测算法。首先,引入缓存机制存储无标注图像和带有伪标注图像的框回归位置信息,避免了后续匹配造成的计算资源浪费。其次,设计混合数据增强策略,将缓存的伪标签图像与无标签图像混合输入学生模型,以增强模型对新数据的泛化能力,并使图像的尺度分布更加均衡。MAM算法不受目标检测模型的限制,并且更好地保持了目标框的一致性,避免了计算一致性损失。实验结果表明,MAM算法相比其他全监督学习和半监督学习算法更具优越性,在自建的非结构化道路缺陷数据集Defect上,在标注比例为10%、20%和30%的场景下,MAM算法的均值平均精度(mAP)相比于Soft Teacher算法分别提升了6.8、11.1和6.0百分点,在自建的非结构化道路坑洼数据集Pothole上,在标注比例为15%和30%的场景下,MAM算法的mAP相比于Soft Teacher算法分别提升了5.8和4.3百分点。展开更多
道路场景目标检测是智慧交通领域的重要组成部分,直接关系到众多智慧交通应用性技术的实施.然而,现有道路场景目标检测域泛化技术普遍存在域不变特征提取不充分、检测精度不高和泛化能力弱的问题.针对此问题,提出复杂天气条件下道路场...道路场景目标检测是智慧交通领域的重要组成部分,直接关系到众多智慧交通应用性技术的实施.然而,现有道路场景目标检测域泛化技术普遍存在域不变特征提取不充分、检测精度不高和泛化能力弱的问题.针对此问题,提出复杂天气条件下道路场景目标检测的域泛化方法.设计了道路场景域不变特征生成模型,分别提取源域图像的域内不变特征和域间不变特征,并生成更具多样性的复杂天气条件下的道路场景域不变特征,以提高目标检测模型的泛化能力;在此基础上,设计了道路场景目标检测域泛化模型,引入自蒸馏机制,使目标检测模型提取的特征拥有丰富的域不变特征,以进一步增强泛化能力,从而提高目标检测模型的检测精度.实验结果表明,所提出的目标检测域泛化模型性能与对比模型相比有明显提升,能显著提高目标检测模型的泛化能力和检测精度,其中F1-score较基线目标检测模型提升0.042~0.051,均值平均精度(mean average precision,mAP)提升3.0%~5.9%,证明了所提出的目标检测域泛化方法的有效性和优越性.展开更多
基金supported by the National Natural Science Foundation of China(No.62103298)。
文摘Aiming at the problems of low detection accuracy and large model size of existing object detection algorithms applied to complex road scenes,an improved you only look once version 8(YOLOv8)object detection algorithm for infrared images,F-YOLOv8,is proposed.First,a spatial-to-depth network replaces the traditional backbone network's strided convolution or pooling layer.At the same time,it combines with the channel attention mechanism so that the neural network focuses on the channels with large weight values to better extract low-resolution image feature information;then an improved feature pyramid network of lightweight bidirectional feature pyramid network(L-BiFPN)is proposed,which can efficiently fuse features of different scales.In addition,a loss function of insertion of union based on the minimum point distance(MPDIoU)is introduced for bounding box regression,which obtains faster convergence speed and more accurate regression results.Experimental results on the FLIR dataset show that the improved algorithm can accurately detect infrared road targets in real time with 3%and 2.2%enhancement in mean average precision at 50%IoU(mAP50)and mean average precision at 50%—95%IoU(mAP50-95),respectively,and 38.1%,37.3%and 16.9%reduction in the number of model parameters,the model weight,and floating-point operations per second(FLOPs),respectively.To further demonstrate the detection capability of the improved algorithm,it is tested on the public dataset PASCAL VOC,and the results show that F-YOLO has excellent generalized detection performance.
文摘主流道路车辆目标检测算法在复杂环境下对小目标识别精度低,易因遮挡和定位不准确造成漏检、误检。提出了改进版YOLOv5算法。针对道路上的小目标,改进Head检测层结构,添加大尺度目标检测层,提高道路上小目标检测精度。为适应目标的形状和尺度变化多样,在颈部网络引入全维动态卷积(Omni-Dimensional Dynamic Convolution,ODConv),对原卷积模块进行替换,提高特征提取能力。为了充分利用全局信息,在颈部网络引入全局注意力机制(Global Attention Mechanism,GAM),提升特征提取能力。针对定位精度问题,引入MPDIoU损失函数,使预测框与真实框更加符合。实验结果表明,改进的YOLOv5算法在自动驾驶数据集KITTI上平均精度均值(mean Average Precision,mAP)达到88.7%,相较于基准模型提高了2%,每秒帧数(Frames per Second,FPS)提升了12%。改进算法的检测精度更高,检测速度更快,有效改善了复杂道路条件下的目标检测问题。
文摘非结构化道路的缺陷目标检测任务对道路交通安全具有重要意义,但检测所需的标注数据集相对有限。为了解决非结构化道路标注数据集缺乏以及现有模型对无标注数据学习能力不足的问题,提出一种MAM(Multi-Augmentation with Memory)半监督目标检测算法。首先,引入缓存机制存储无标注图像和带有伪标注图像的框回归位置信息,避免了后续匹配造成的计算资源浪费。其次,设计混合数据增强策略,将缓存的伪标签图像与无标签图像混合输入学生模型,以增强模型对新数据的泛化能力,并使图像的尺度分布更加均衡。MAM算法不受目标检测模型的限制,并且更好地保持了目标框的一致性,避免了计算一致性损失。实验结果表明,MAM算法相比其他全监督学习和半监督学习算法更具优越性,在自建的非结构化道路缺陷数据集Defect上,在标注比例为10%、20%和30%的场景下,MAM算法的均值平均精度(mAP)相比于Soft Teacher算法分别提升了6.8、11.1和6.0百分点,在自建的非结构化道路坑洼数据集Pothole上,在标注比例为15%和30%的场景下,MAM算法的mAP相比于Soft Teacher算法分别提升了5.8和4.3百分点。
文摘道路场景目标检测是智慧交通领域的重要组成部分,直接关系到众多智慧交通应用性技术的实施.然而,现有道路场景目标检测域泛化技术普遍存在域不变特征提取不充分、检测精度不高和泛化能力弱的问题.针对此问题,提出复杂天气条件下道路场景目标检测的域泛化方法.设计了道路场景域不变特征生成模型,分别提取源域图像的域内不变特征和域间不变特征,并生成更具多样性的复杂天气条件下的道路场景域不变特征,以提高目标检测模型的泛化能力;在此基础上,设计了道路场景目标检测域泛化模型,引入自蒸馏机制,使目标检测模型提取的特征拥有丰富的域不变特征,以进一步增强泛化能力,从而提高目标检测模型的检测精度.实验结果表明,所提出的目标检测域泛化模型性能与对比模型相比有明显提升,能显著提高目标检测模型的泛化能力和检测精度,其中F1-score较基线目标检测模型提升0.042~0.051,均值平均精度(mean average precision,mAP)提升3.0%~5.9%,证明了所提出的目标检测域泛化方法的有效性和优越性.
