Focused on the task of fast and accurate armored target detection in ground battlefield,a detection method based on multi-scale representation network(MS-RN) and shape-fixed Guided Anchor(SF-GA)scheme is proposed.Firs...Focused on the task of fast and accurate armored target detection in ground battlefield,a detection method based on multi-scale representation network(MS-RN) and shape-fixed Guided Anchor(SF-GA)scheme is proposed.Firstly,considering the large-scale variation and camouflage of armored target,a new MS-RN integrating contextual information in battlefield environment is designed.The MS-RN extracts deep features from templates with different scales and strengthens the detection ability of small targets.Armored targets of different sizes are detected on different representation features.Secondly,aiming at the accuracy and real-time detection requirements,improved shape-fixed Guided Anchor is used on feature maps of different scales to recommend regions of interests(ROIs).Different from sliding or random anchor,the SF-GA can filter out 80% of the regions while still improving the recall.A special detection dataset for armored target,named Armored Target Dataset(ARTD),is constructed,based on which the comparable experiments with state-of-art detection methods are conducted.Experimental results show that the proposed method achieves outstanding performance in detection accuracy and efficiency,especially when small armored targets are involved.展开更多
In response to the challenge of low detection accuracy and susceptibility to missed and false detections of small targets in unmanned aerial vehicles(UAVs)aerial images,an improved UAV image target detection algorithm...In response to the challenge of low detection accuracy and susceptibility to missed and false detections of small targets in unmanned aerial vehicles(UAVs)aerial images,an improved UAV image target detection algorithm based on YOLOv8 was proposed in this study.To begin with,the CoordAtt attention mechanism was employed to enhance the feature extraction capability of the backbone network,thereby reducing interference from backgrounds.Additionally,the BiFPN feature fusion network with an added small object detection layer was used to enhance the model's ability to perceive for small objects.Furthermore,a multi-level fusion module was designed and proposed to effectively integrate shallow and deep information.The use of an enhanced MPDIoU loss function further improved detection performance.The experimental results based on the publicly available VisDrone2019 dataset showed that the improved model outperformed the YOLOv8 baseline model,mAP@0.5 improved by 20%,and the improved method improved the detection accuracy of the model for small targets.展开更多
Detection of floating garbage in inland rivers is crucial for water environmental protection,as it effectively reduces ecological damage and ensures the safety of water resources.To address the inefficiency of traditi...Detection of floating garbage in inland rivers is crucial for water environmental protection,as it effectively reduces ecological damage and ensures the safety of water resources.To address the inefficiency of traditional cleanup methods and the challenges in detecting small targets,an improved YOLOv5 object detection model was proposed in this study.In order to enhance the model’s sensitivity to small targets and mitigate the impact of redundant information on detection performance,a bi-level routing attention mechanism was introduced and embedded into the backbone network.Additionally,a multi-scale detection head was incorporated into the model,allowing for more comprehensive coverage of floating garbage of various sizes through multi-scale feature extraction and detection.The Focal-EIoU loss function was also employed to optimize the model parameters,improving localization accuracy.Experimental results on the publicly available FloW_Img dataset demonstrated that the improved YOLOv5 model outperforms the original YOLOv5 model in terms of precision and recall,achieving a mAP(mean average precision)of 86.12%,with significant improvements and faster convergence.展开更多
In this paper,based on a bidirectional parallel multi-branch feature pyramid network(BPMFPN),a novel one-stage object detector called BPMFPN Det is proposed for real-time detection of ground multi-scale targets by swa...In this paper,based on a bidirectional parallel multi-branch feature pyramid network(BPMFPN),a novel one-stage object detector called BPMFPN Det is proposed for real-time detection of ground multi-scale targets by swarm unmanned aerial vehicles(UAVs).First,the bidirectional parallel multi-branch convolution modules are used to construct the feature pyramid to enhance the feature expression abilities of different scale feature layers.Next,the feature pyramid is integrated into the single-stage object detection framework to ensure real-time performance.In order to validate the effectiveness of the proposed algorithm,experiments are conducted on four datasets.For the PASCAL VOC dataset,the proposed algorithm achieves the mean average precision(mAP)of 85.4 on the VOC 2007 test set.With regard to the detection in optical remote sensing(DIOR)dataset,the proposed algorithm achieves 73.9 mAP.For vehicle detection in aerial imagery(VEDAI)dataset,the detection accuracy of small land vehicle(slv)targets reaches 97.4 mAP.For unmanned aerial vehicle detection and tracking(UAVDT)dataset,the proposed BPMFPN Det achieves the mAP of 48.75.Compared with the previous state-of-the-art methods,the results obtained by the proposed algorithm are more competitive.The experimental results demonstrate that the proposed algorithm can effectively solve the problem of real-time detection of ground multi-scale targets in aerial images of swarm UAVs.展开更多
在复杂自然场景的端到端文本识别中,由于文本和背景难以区分,文本检测的位置信息和识别的语义信息不匹配,无法有效利用检测和识别之间的相关性.针对该问题,本文提出双域感知下多方显式信息协同的自然场景端到端文本识别方法(Multi-party...在复杂自然场景的端到端文本识别中,由于文本和背景难以区分,文本检测的位置信息和识别的语义信息不匹配,无法有效利用检测和识别之间的相关性.针对该问题,本文提出双域感知下多方显式信息协同的自然场景端到端文本识别方法(Multi-party Synergetic explicit Information with Dual-domain Awareness text spotting,MSIDA),通过强化文本区域特征和边缘纹理,利用文本检测和识别特征之间的协同作用提高端到端文本识别性能.首先,设计融合文本空间和方向信息的双域感知模块(Dual-Domain Awareness,DDA),增强文本实例的视觉特征信息;其次,提出多方显式信息协同模块(Multi-party Explicit Information Synergy,MEIS)提取编码特征中的显式信息,通过匹配对齐用于检测和识别的位置、分类和字符多方信息生成候选文本实例;最后,协同特征通过解码器引导可学习的查询序列获得文本检测和识别的结果 .相比最新的DeepSolo(Decoder with explicit points Solo)方法,在Total-Text、ICDAR 2015和CTW1500数据集上,MSIDA模型的准确率分别提升0.8%、0.8%和0.4%.代码和数据集在https://github.com/msida2024/MSIDA.git可以获取.展开更多
为了有效检测识别被篡改的古籍文字图像,提出一种可用于古籍文字图像篡改的检测识别模型MDAS-Net。首先在边缘监督分支中提出一种全新的特征融合方式即混合注意力块,以更好地提取图像中的多尺度目标信息;其次,针对边缘监督分支和噪声敏...为了有效检测识别被篡改的古籍文字图像,提出一种可用于古籍文字图像篡改的检测识别模型MDAS-Net。首先在边缘监督分支中提出一种全新的特征融合方式即混合注意力块,以更好地提取图像中的多尺度目标信息;其次,针对边缘监督分支和噪声敏感分支的特征融合设计一种特征传递模块E-2-N/N-2-E Help Block,促进2个分支间的信息交流,以得到更高质量的融合特征。为了验证模型的有效性,创建古籍图像篡改数据集,并联合篡改图像文本数据集(TTI)进行对比实验和消融实验。结果表明,MDAS-Net模型在古籍文字图像篡改区域检测效果良好,受试者工作特性曲线下的面积(AUC)达到了0.852,F_(1)值达到了0.784,并证明了MDAS-Net在检测古籍文字图像篡改方面的实用性。展开更多
基金supported by the National Key Research and Development Program of China under grant 2016YFC0802904National Natural Science Foundation of China under grant61671470the Postdoctoral Science Foundation Funded Project of China under grant 2017M623423。
文摘Focused on the task of fast and accurate armored target detection in ground battlefield,a detection method based on multi-scale representation network(MS-RN) and shape-fixed Guided Anchor(SF-GA)scheme is proposed.Firstly,considering the large-scale variation and camouflage of armored target,a new MS-RN integrating contextual information in battlefield environment is designed.The MS-RN extracts deep features from templates with different scales and strengthens the detection ability of small targets.Armored targets of different sizes are detected on different representation features.Secondly,aiming at the accuracy and real-time detection requirements,improved shape-fixed Guided Anchor is used on feature maps of different scales to recommend regions of interests(ROIs).Different from sliding or random anchor,the SF-GA can filter out 80% of the regions while still improving the recall.A special detection dataset for armored target,named Armored Target Dataset(ARTD),is constructed,based on which the comparable experiments with state-of-art detection methods are conducted.Experimental results show that the proposed method achieves outstanding performance in detection accuracy and efficiency,especially when small armored targets are involved.
文摘In response to the challenge of low detection accuracy and susceptibility to missed and false detections of small targets in unmanned aerial vehicles(UAVs)aerial images,an improved UAV image target detection algorithm based on YOLOv8 was proposed in this study.To begin with,the CoordAtt attention mechanism was employed to enhance the feature extraction capability of the backbone network,thereby reducing interference from backgrounds.Additionally,the BiFPN feature fusion network with an added small object detection layer was used to enhance the model's ability to perceive for small objects.Furthermore,a multi-level fusion module was designed and proposed to effectively integrate shallow and deep information.The use of an enhanced MPDIoU loss function further improved detection performance.The experimental results based on the publicly available VisDrone2019 dataset showed that the improved model outperformed the YOLOv8 baseline model,mAP@0.5 improved by 20%,and the improved method improved the detection accuracy of the model for small targets.
文摘Detection of floating garbage in inland rivers is crucial for water environmental protection,as it effectively reduces ecological damage and ensures the safety of water resources.To address the inefficiency of traditional cleanup methods and the challenges in detecting small targets,an improved YOLOv5 object detection model was proposed in this study.In order to enhance the model’s sensitivity to small targets and mitigate the impact of redundant information on detection performance,a bi-level routing attention mechanism was introduced and embedded into the backbone network.Additionally,a multi-scale detection head was incorporated into the model,allowing for more comprehensive coverage of floating garbage of various sizes through multi-scale feature extraction and detection.The Focal-EIoU loss function was also employed to optimize the model parameters,improving localization accuracy.Experimental results on the publicly available FloW_Img dataset demonstrated that the improved YOLOv5 model outperforms the original YOLOv5 model in terms of precision and recall,achieving a mAP(mean average precision)of 86.12%,with significant improvements and faster convergence.
文摘In this paper,based on a bidirectional parallel multi-branch feature pyramid network(BPMFPN),a novel one-stage object detector called BPMFPN Det is proposed for real-time detection of ground multi-scale targets by swarm unmanned aerial vehicles(UAVs).First,the bidirectional parallel multi-branch convolution modules are used to construct the feature pyramid to enhance the feature expression abilities of different scale feature layers.Next,the feature pyramid is integrated into the single-stage object detection framework to ensure real-time performance.In order to validate the effectiveness of the proposed algorithm,experiments are conducted on four datasets.For the PASCAL VOC dataset,the proposed algorithm achieves the mean average precision(mAP)of 85.4 on the VOC 2007 test set.With regard to the detection in optical remote sensing(DIOR)dataset,the proposed algorithm achieves 73.9 mAP.For vehicle detection in aerial imagery(VEDAI)dataset,the detection accuracy of small land vehicle(slv)targets reaches 97.4 mAP.For unmanned aerial vehicle detection and tracking(UAVDT)dataset,the proposed BPMFPN Det achieves the mAP of 48.75.Compared with the previous state-of-the-art methods,the results obtained by the proposed algorithm are more competitive.The experimental results demonstrate that the proposed algorithm can effectively solve the problem of real-time detection of ground multi-scale targets in aerial images of swarm UAVs.
文摘在复杂自然场景的端到端文本识别中,由于文本和背景难以区分,文本检测的位置信息和识别的语义信息不匹配,无法有效利用检测和识别之间的相关性.针对该问题,本文提出双域感知下多方显式信息协同的自然场景端到端文本识别方法(Multi-party Synergetic explicit Information with Dual-domain Awareness text spotting,MSIDA),通过强化文本区域特征和边缘纹理,利用文本检测和识别特征之间的协同作用提高端到端文本识别性能.首先,设计融合文本空间和方向信息的双域感知模块(Dual-Domain Awareness,DDA),增强文本实例的视觉特征信息;其次,提出多方显式信息协同模块(Multi-party Explicit Information Synergy,MEIS)提取编码特征中的显式信息,通过匹配对齐用于检测和识别的位置、分类和字符多方信息生成候选文本实例;最后,协同特征通过解码器引导可学习的查询序列获得文本检测和识别的结果 .相比最新的DeepSolo(Decoder with explicit points Solo)方法,在Total-Text、ICDAR 2015和CTW1500数据集上,MSIDA模型的准确率分别提升0.8%、0.8%和0.4%.代码和数据集在https://github.com/msida2024/MSIDA.git可以获取.
文摘为了有效检测识别被篡改的古籍文字图像,提出一种可用于古籍文字图像篡改的检测识别模型MDAS-Net。首先在边缘监督分支中提出一种全新的特征融合方式即混合注意力块,以更好地提取图像中的多尺度目标信息;其次,针对边缘监督分支和噪声敏感分支的特征融合设计一种特征传递模块E-2-N/N-2-E Help Block,促进2个分支间的信息交流,以得到更高质量的融合特征。为了验证模型的有效性,创建古籍图像篡改数据集,并联合篡改图像文本数据集(TTI)进行对比实验和消融实验。结果表明,MDAS-Net模型在古籍文字图像篡改区域检测效果良好,受试者工作特性曲线下的面积(AUC)达到了0.852,F_(1)值达到了0.784,并证明了MDAS-Net在检测古籍文字图像篡改方面的实用性。
