Addressing the challenges in detecting surface floating litter in artificial lakes,including complex environments,uneven illumination,and susceptibility to noise andweather,this paper proposes an efficient and lightwe...Addressing the challenges in detecting surface floating litter in artificial lakes,including complex environments,uneven illumination,and susceptibility to noise andweather,this paper proposes an efficient and lightweight Ghost-YOLO(You Only Look Once)v8 algorithm.The algorithmintegrates advanced attention mechanisms and a smalltarget detection head to significantly enhance detection performance and efficiency.Firstly,an SE(Squeeze-and-Excitation)mechanism is incorporated into the backbone network to fortify the extraction of resilient features and precise target localization.This mechanism models feature channel dependencies,enabling adaptive adjustment of channel importance,thereby improving recognition of floating litter targets.Secondly,a 160×160 small-target detection layer is designed in the feature fusion neck to mitigate semantic information loss due to varying target scales.This design enhances the fusion of deep and shallow semantic information,improving small target feature representation and enabling better capture and identification of tiny floating litter.Thirdly,to balance performance and efficiency,the GhostConv module replaces part of the conventional convolutions in the feature fusion neck.Additionally,a novel C2fGhost(CSPDarknet53 to 2-Stage Feature Pyramid Networks Ghost)module is introduced to further reduce network parameters.Lastly,to address the challenge of occlusion,a newloss function,WIoU(Wise Intersection over Union)v3 incorporating a flexible and non-monotonic concentration approach,is adopted to improve detection rates for surface floating litter.The outcomes of the experiments demonstrate that the Ghost-YOLO v8 model proposed in this paper performs well in the dataset Marine,significantly enhances precision and recall by 3.3 and 7.6 percentage points,respectively,in contrast with the base model,mAP@0.5 and mAP 0.5:0.95 improve by 5.3 and 4.4 percentage points and reduces the computational volume by 1.88MB,the FPS value hardly decreases,and the efficient real-time identification of floating debris on the water’s surface can be achieved costeffectively.展开更多
The traditional small target detection algorithm often results in a high false alarm rate on the sea surface background. To address this issue, a small target detection method based on guided filtering and local avera...The traditional small target detection algorithm often results in a high false alarm rate on the sea surface background. To address this issue, a small target detection method based on guided filtering and local average gray level difference was proposed in this paper for the sea surface. Firstly, the method enhanced the details of the small targets by employing guided filtering to suppress the background clutter and noise in the sea surface image. Subsequently, the local average gray level difference of each point in the image was calculated to further distinguish the targets from other interference points. Finally, the threshold segmentation method was utilized to obtain the actual small targets on the sea surface. After conducting experiments on various sea surface scenes, the LSCRG, BSF, and ROC curve were computed for the proposed method and five other algorithms. Comparative analysis with BS, Top-hat, TDLMS, Max-median, and LCM demonstrates the superiority of the proposed method for infrared small target detection on the sea surface.展开更多
In this paper,a detection method combining Cameron decomposition based on polarization scattering characteristics in sea clutter background is proposed.Firstly,the Cameron decomposition is exploited to fuse the radar ...In this paper,a detection method combining Cameron decomposition based on polarization scattering characteristics in sea clutter background is proposed.Firstly,the Cameron decomposition is exploited to fuse the radar echoes of full polarization channels at the data level.Due to the artificial material structure on the surface of the target,it can be shown that the non-reciprocity of the target cell is stronger than that of the clutter cell.Then,based on the analysis of the decomposition results,a new feature with scattering geometry characteristics in polarization domain,denoted as Cameron polarization decomposition scattering weight(CPD-SW),is extracted as the test statistic,which can achieve more detailed descriptions of the clutter scattering characteristics utilizing the difference between their scattering types.Finally,the superiority of the proposed CPD-SW detector over traditional detectors in improving detection performance is verified by the IPIX measured dataset,which has strong stability under short-time observation in threshold detection and can also improve the separability of feature space zin anomaly detection.展开更多
海杂波背景下的海上小目标是海洋雷达探测的重难点。针对特征空间内海杂波与小目标特征可分性问题,提出了量化特征之间可分性的度量标准——重叠系数。通过开展对海探测试验获取的2~5级海况实测数据,分别提取时域特征相对平均幅度(Relat...海杂波背景下的海上小目标是海洋雷达探测的重难点。针对特征空间内海杂波与小目标特征可分性问题,提出了量化特征之间可分性的度量标准——重叠系数。通过开展对海探测试验获取的2~5级海况实测数据,分别提取时域特征相对平均幅度(Relative Average Amplitude,RAA)、相对峰值峰高(Relative Peak Height,RPH)、时域熵值均值(Time domain Entropy Mean,TEM),频域特征相对多普勒峰高(Relative Doppler Peak Height,RDPH)、相对多普勒向量熵(Relative Vector Entropy,RVE)、频域熵值二阶矩(Second moment of Frequency domain Entropy,SOFE),计算出重叠系数。通过特征检测器进行检测性能对比,低海况下,相对平均幅度、相对峰值峰高、时域熵值均值、相对多普勒峰高、频域熵值二阶矩特征之间重叠系数均在0.3以下,对应特征检测器的检测概率均在85%以上;高海况下其特征之间重叠系数均在0.7以上,对应特征检测器的检测概率均在50%以下。相对多普勒向量熵在4种海况下可分性较小,其对应的特征检测器性能较差。结果验证了重叠系数在一维特征选择的应用可行性,为多特征融合目标检测提供了一定支持。展开更多
基金Supported by the fund of the Henan Province Science and Technology Research Project(No.242102210213).
文摘Addressing the challenges in detecting surface floating litter in artificial lakes,including complex environments,uneven illumination,and susceptibility to noise andweather,this paper proposes an efficient and lightweight Ghost-YOLO(You Only Look Once)v8 algorithm.The algorithmintegrates advanced attention mechanisms and a smalltarget detection head to significantly enhance detection performance and efficiency.Firstly,an SE(Squeeze-and-Excitation)mechanism is incorporated into the backbone network to fortify the extraction of resilient features and precise target localization.This mechanism models feature channel dependencies,enabling adaptive adjustment of channel importance,thereby improving recognition of floating litter targets.Secondly,a 160×160 small-target detection layer is designed in the feature fusion neck to mitigate semantic information loss due to varying target scales.This design enhances the fusion of deep and shallow semantic information,improving small target feature representation and enabling better capture and identification of tiny floating litter.Thirdly,to balance performance and efficiency,the GhostConv module replaces part of the conventional convolutions in the feature fusion neck.Additionally,a novel C2fGhost(CSPDarknet53 to 2-Stage Feature Pyramid Networks Ghost)module is introduced to further reduce network parameters.Lastly,to address the challenge of occlusion,a newloss function,WIoU(Wise Intersection over Union)v3 incorporating a flexible and non-monotonic concentration approach,is adopted to improve detection rates for surface floating litter.The outcomes of the experiments demonstrate that the Ghost-YOLO v8 model proposed in this paper performs well in the dataset Marine,significantly enhances precision and recall by 3.3 and 7.6 percentage points,respectively,in contrast with the base model,mAP@0.5 and mAP 0.5:0.95 improve by 5.3 and 4.4 percentage points and reduces the computational volume by 1.88MB,the FPS value hardly decreases,and the efficient real-time identification of floating debris on the water’s surface can be achieved costeffectively.
文摘The traditional small target detection algorithm often results in a high false alarm rate on the sea surface background. To address this issue, a small target detection method based on guided filtering and local average gray level difference was proposed in this paper for the sea surface. Firstly, the method enhanced the details of the small targets by employing guided filtering to suppress the background clutter and noise in the sea surface image. Subsequently, the local average gray level difference of each point in the image was calculated to further distinguish the targets from other interference points. Finally, the threshold segmentation method was utilized to obtain the actual small targets on the sea surface. After conducting experiments on various sea surface scenes, the LSCRG, BSF, and ROC curve were computed for the proposed method and five other algorithms. Comparative analysis with BS, Top-hat, TDLMS, Max-median, and LCM demonstrates the superiority of the proposed method for infrared small target detection on the sea surface.
基金supported by the National Natural Science Foundation of China(62201251)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(22KJB510024)the Open Fund for the Hangzhou Institute of Technology Academician Workstation at Xidian University(XH-KY-202306-0291)。
文摘In this paper,a detection method combining Cameron decomposition based on polarization scattering characteristics in sea clutter background is proposed.Firstly,the Cameron decomposition is exploited to fuse the radar echoes of full polarization channels at the data level.Due to the artificial material structure on the surface of the target,it can be shown that the non-reciprocity of the target cell is stronger than that of the clutter cell.Then,based on the analysis of the decomposition results,a new feature with scattering geometry characteristics in polarization domain,denoted as Cameron polarization decomposition scattering weight(CPD-SW),is extracted as the test statistic,which can achieve more detailed descriptions of the clutter scattering characteristics utilizing the difference between their scattering types.Finally,the superiority of the proposed CPD-SW detector over traditional detectors in improving detection performance is verified by the IPIX measured dataset,which has strong stability under short-time observation in threshold detection and can also improve the separability of feature space zin anomaly detection.
文摘海杂波背景下的海上小目标是海洋雷达探测的重难点。针对特征空间内海杂波与小目标特征可分性问题,提出了量化特征之间可分性的度量标准——重叠系数。通过开展对海探测试验获取的2~5级海况实测数据,分别提取时域特征相对平均幅度(Relative Average Amplitude,RAA)、相对峰值峰高(Relative Peak Height,RPH)、时域熵值均值(Time domain Entropy Mean,TEM),频域特征相对多普勒峰高(Relative Doppler Peak Height,RDPH)、相对多普勒向量熵(Relative Vector Entropy,RVE)、频域熵值二阶矩(Second moment of Frequency domain Entropy,SOFE),计算出重叠系数。通过特征检测器进行检测性能对比,低海况下,相对平均幅度、相对峰值峰高、时域熵值均值、相对多普勒峰高、频域熵值二阶矩特征之间重叠系数均在0.3以下,对应特征检测器的检测概率均在85%以上;高海况下其特征之间重叠系数均在0.7以上,对应特征检测器的检测概率均在50%以下。相对多普勒向量熵在4种海况下可分性较小,其对应的特征检测器性能较差。结果验证了重叠系数在一维特征选择的应用可行性,为多特征融合目标检测提供了一定支持。
