A method of multi-block Single Shot Multi Box Detector(SSD)based on small object detection is proposed to the railway scene of unmanned aerial vehicle surveillance.To address the limitation of small object detection,a...A method of multi-block Single Shot Multi Box Detector(SSD)based on small object detection is proposed to the railway scene of unmanned aerial vehicle surveillance.To address the limitation of small object detection,a multi-block SSD mechanism,which consists of three steps,is designed.First,the original input images are segmented into several overlapped patches.Second,each patch is separately fed into an SSD to detect the objects.Third,the patches are merged together through two stages.In the first stage,the truncated object of the sub-layer detection result is spliced.In the second stage,a sub-layer suppression and filtering algorithm applying the concept of non-maximum suppression is utilized to remove the overlapped boxes of sub-layers.The boxes that are not detected in the main-layer are retained.In addition,no sufficient labeled training samples of railway circumstance are available,thereby hindering the deployment of SSD.A two-stage training strategy leveraging to transfer learning is adopted to solve this issue.The deep learning model is preliminarily trained using labeled data of numerous auxiliaries,and then it is refined using only a few samples of railway scene.A railway spot in China,which is easily damaged by landslides,is investigated as a case study.Experimental results show that the proposed multi-block SSD method produces an overall accuracy of 96.6%and obtains an improvement of up to 9.2%compared with the traditional SSD.展开更多
A robust TV logo detection method based on the modified single shot multibox detector (SSD) is presented. Unlike most other existing methods which can only detect the TV logo from video frames, the proposed method can...A robust TV logo detection method based on the modified single shot multibox detector (SSD) is presented. Unlike most other existing methods which can only detect the TV logo from video frames, the proposed method can also detect the TV logo from photo pictures taken by smartphones or other smart terminals. Firstly, using a simple and effective way of collecting and labelling TV logo, a large-scale TV logo dataset used to train the detection model is built. Then, parameters and loss function of SSD are modified to make it more suitable for the task of TV logo detection. Moreover, a soft-NMS algorithm is introduced to remove the redundant overlapping boxes and obtain the final output box. And also an approach for hard example mining is designed to improve the detection accuracy. Finally, extensive comparison experiments are carried out which take into consideration different image resolutions, logo positions and environmental factors existing in real-world applications. Experimental results demonstrate that the proposed method achieve superior performances in robustness compared to other state-of-the-art methods.展开更多
目的海面目标检测图像中的小目标数量居多,而基于深度学习的目标检测方法通常针对通用目标数据集设计检测模型,对图像中的小目标检测效果并不理想。使用一般目标检测模型检测海面目标图像的特征时,通常会出现小目标漏检情况,而一些特定...目的海面目标检测图像中的小目标数量居多,而基于深度学习的目标检测方法通常针对通用目标数据集设计检测模型,对图像中的小目标检测效果并不理想。使用一般目标检测模型检测海面目标图像的特征时,通常会出现小目标漏检情况,而一些特定的小目标检测模型对海面目标的检测效果还有待验证。为此,在标准的SSD(single shot multi Box detector)目标检测模型基础上,结合Xception深度可分卷积,提出一种轻量SSD模型用于海面目标检测。方法在标准的SSD目标检测模型基础上,使用基于Xception网络的深度可分卷积特征提取网络替换VGG-16(Visual Geometry Group network-16)骨干网络,通过控制变量来对比不同网络的检测效果;在特征提取网络中的exit flow层和Conv1层引入轻量级注意力机制模块来提高检测精度,并与在其他层引入轻量级注意力机制模块的模型进行检测效果对比;使用注意力机制改进的轻量SSD目标检测模型和其他几种模型分别对海面目标检测数据集中的小目标和正常目标进行测试。结果为证明本文模型的有效性,进行了多组对比实验。实验结果表明,模型轻量化导致特征表达能力降低,从而影响检测精度。相对于标准的SSD目标检测模型,本文模型在参数量降低16.26%、浮点运算量降低15.65%的情况下,浮标的平均检测精度提高了1.1%,漏检率减小了3%,平均精度均值(mean average precision,mAP)提高了0.51%,同时,保证了船的平均检测精度,并保证其漏检率不升高,在对数据集中的小目标进行测试时,本文模型也表现出较好的检测效果。结论本文提出的海面小目标检测模型,能够在压缩模型的同时,保证模型的检测速度和检测精度,达到网络轻量化的效果,并且降低了小目标的漏检率,可以有效实现对海面小目标的检测。展开更多
基金supported by Beijing Natural Science Foundation,China(No.4182020)Open Fund of State Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,China(No.17E01)Key Laboratory for Health Monitoring and Control of Large Structures,Shijiazhuang,China(No.KLLSHMC1901)。
文摘A method of multi-block Single Shot Multi Box Detector(SSD)based on small object detection is proposed to the railway scene of unmanned aerial vehicle surveillance.To address the limitation of small object detection,a multi-block SSD mechanism,which consists of three steps,is designed.First,the original input images are segmented into several overlapped patches.Second,each patch is separately fed into an SSD to detect the objects.Third,the patches are merged together through two stages.In the first stage,the truncated object of the sub-layer detection result is spliced.In the second stage,a sub-layer suppression and filtering algorithm applying the concept of non-maximum suppression is utilized to remove the overlapped boxes of sub-layers.The boxes that are not detected in the main-layer are retained.In addition,no sufficient labeled training samples of railway circumstance are available,thereby hindering the deployment of SSD.A two-stage training strategy leveraging to transfer learning is adopted to solve this issue.The deep learning model is preliminarily trained using labeled data of numerous auxiliaries,and then it is refined using only a few samples of railway scene.A railway spot in China,which is easily damaged by landslides,is investigated as a case study.Experimental results show that the proposed multi-block SSD method produces an overall accuracy of 96.6%and obtains an improvement of up to 9.2%compared with the traditional SSD.
基金Supported by the National Natural Science Foundationof China(No.61702466)“Double Tops” Discipline Construction Project
文摘A robust TV logo detection method based on the modified single shot multibox detector (SSD) is presented. Unlike most other existing methods which can only detect the TV logo from video frames, the proposed method can also detect the TV logo from photo pictures taken by smartphones or other smart terminals. Firstly, using a simple and effective way of collecting and labelling TV logo, a large-scale TV logo dataset used to train the detection model is built. Then, parameters and loss function of SSD are modified to make it more suitable for the task of TV logo detection. Moreover, a soft-NMS algorithm is introduced to remove the redundant overlapping boxes and obtain the final output box. And also an approach for hard example mining is designed to improve the detection accuracy. Finally, extensive comparison experiments are carried out which take into consideration different image resolutions, logo positions and environmental factors existing in real-world applications. Experimental results demonstrate that the proposed method achieve superior performances in robustness compared to other state-of-the-art methods.
文摘目的海面目标检测图像中的小目标数量居多,而基于深度学习的目标检测方法通常针对通用目标数据集设计检测模型,对图像中的小目标检测效果并不理想。使用一般目标检测模型检测海面目标图像的特征时,通常会出现小目标漏检情况,而一些特定的小目标检测模型对海面目标的检测效果还有待验证。为此,在标准的SSD(single shot multi Box detector)目标检测模型基础上,结合Xception深度可分卷积,提出一种轻量SSD模型用于海面目标检测。方法在标准的SSD目标检测模型基础上,使用基于Xception网络的深度可分卷积特征提取网络替换VGG-16(Visual Geometry Group network-16)骨干网络,通过控制变量来对比不同网络的检测效果;在特征提取网络中的exit flow层和Conv1层引入轻量级注意力机制模块来提高检测精度,并与在其他层引入轻量级注意力机制模块的模型进行检测效果对比;使用注意力机制改进的轻量SSD目标检测模型和其他几种模型分别对海面目标检测数据集中的小目标和正常目标进行测试。结果为证明本文模型的有效性,进行了多组对比实验。实验结果表明,模型轻量化导致特征表达能力降低,从而影响检测精度。相对于标准的SSD目标检测模型,本文模型在参数量降低16.26%、浮点运算量降低15.65%的情况下,浮标的平均检测精度提高了1.1%,漏检率减小了3%,平均精度均值(mean average precision,mAP)提高了0.51%,同时,保证了船的平均检测精度,并保证其漏检率不升高,在对数据集中的小目标进行测试时,本文模型也表现出较好的检测效果。结论本文提出的海面小目标检测模型,能够在压缩模型的同时,保证模型的检测速度和检测精度,达到网络轻量化的效果,并且降低了小目标的漏检率,可以有效实现对海面小目标的检测。
