Low-angle estimation for very high frequency(VHF)radar is a difficult problem due to the multipath effect in the radar field,especially in complex scenarios where the reflection condition is unknown.To deal with this ...Low-angle estimation for very high frequency(VHF)radar is a difficult problem due to the multipath effect in the radar field,especially in complex scenarios where the reflection condition is unknown.To deal with this problem,we propose an algorithm of target height and multipath attenuation joint estimation.The amplitude of the surface reflection coefficient is estimated by the characteristic of the data itself,and it is assumed that there is no reflected signal when the amplitude is very small.The phase of the surface reflection coefficient and the phase difference between the direct and reflected signals are searched as the same part,and this represents the multipath phase attenuation.The Cramer-Rao bound of the proposed algorithm is also derived.Finally,computer simulations and real data processing results show that the proposed algorithm has good estimation performance under complex scenarios and works well with only one snapshot.展开更多
为了解决施工场景下安全帽佩戴检测时,由于人员密集、遮挡和复杂背景等原因造成的小目标漏检和错检的问题,提出一种基于YOLOv8n的双重注意力机制的跨层多尺度安全帽佩戴检测算法。首先,设计微小目标检测头,以提高模型对小目标的检测能力...为了解决施工场景下安全帽佩戴检测时,由于人员密集、遮挡和复杂背景等原因造成的小目标漏检和错检的问题,提出一种基于YOLOv8n的双重注意力机制的跨层多尺度安全帽佩戴检测算法。首先,设计微小目标检测头,以提高模型对小目标的检测能力;其次,在特征提取网络中嵌入双重注意力机制,从而更加关注复杂场景下目标信息的特征捕获;然后,将特征融合网络替换成重参数化泛化特征金字塔网络(RepGFPN)改进后的跨层多尺度特征融合结构S-GFPN(Selective layer Generalized Feature Pyramid Network),以实现小目标特征层信息和其他特征层的多尺度融合,并建立长期的依赖关系,从而抑制背景信息的干扰;最后,采用MPDIOU(Intersection Over Union with Minimum Point Distance)损失函数来解决尺度变化不敏感的问题。在公开数据集GDUT-HWD上的实验结果表明,改进后的模型比YOLOv8n的mAP@0.5提升了3.4个百分点,对蓝色、黄色、白色和红色安全帽的检测精度分别提升了2.0、1.1、4.6和9.1个百分点,在密集、遮挡、小目标、反光和黑暗这5类复杂场景下的可视化检测效果也优于YOLOv8n,为实际施工场景中安全帽佩戴检测提供了一种有效方法。展开更多
The swift evolution of deep learning has greatly benefited the field of intensive aquaculture.Specifically,deep learning-based shrimp larvae detection has offered important technical assistance for counting shrimp lar...The swift evolution of deep learning has greatly benefited the field of intensive aquaculture.Specifically,deep learning-based shrimp larvae detection has offered important technical assistance for counting shrimp larvae and recognizing abnormal behaviors.Firstly,the transparent bodies and small sizes of shrimp larvae,combined with complex scenarios due to variations in light intensity and water turbidity,make it challenging for current detection methods to achieve high accuracy.Secondly,deep learning-based object detection demands substantial computing power and storage space,which restricts its application on edge devices.This paper proposes an efficient one-stage shrimp larvae detection method,FAMDet,specifically designed for complex scenarios in intensive aquaculture.Firstly,different from the ordinary detection methods,it exploits an efficient FasterNet backbone,constructed with partial convolution,to extract effective multi-scale shrimp larvae features.Meanwhile,we construct an adaptively bi-directional fusion neck to integrate high-level semantic information and low-level detail information of shrimp larvae in a matter that sufficiently merges features and further mitigates noise interference.Finally,a decoupled detection head equipped with MPDIoU is used for precise bounding box regression of shrimp larvae.We collected images of shrimp larvae from multiple scenarios and labeled 108,365 targets for experiments.Compared with the ordinary detection methods(Faster RCNN,SSD,RetinaNet,CenterNet,FCOS,DETR,and YOLOX_s),FAMDet has obtained considerable advantages in accuracy,speed,and complexity.Compared with the outstanding one-stage method YOLOv8s,it has improved accuracy while reducing 57%parameters,37%FLOPs,22%inference latency per image on CPU,and 56%storage overhead.Furthermore,FAMDet has still outperformed multiple lightweight methods(EfficientDet,RT-DETR,GhostNetV2,EfficientFormerV2,EfficientViT,and MobileNetV4).In addition,we conducted experiments on the public dataset(VOC 07+12)to further verify the effectiveness of FAMDet.Consequently,the proposed method can effectively alleviate the limitations faced by resource-constrained devices and achieve superior shrimp larvae detection results.展开更多
基金the Fund for Foreign Scholars in University Research and Teaching Programs(the 111 Project)(No.B18039)。
文摘Low-angle estimation for very high frequency(VHF)radar is a difficult problem due to the multipath effect in the radar field,especially in complex scenarios where the reflection condition is unknown.To deal with this problem,we propose an algorithm of target height and multipath attenuation joint estimation.The amplitude of the surface reflection coefficient is estimated by the characteristic of the data itself,and it is assumed that there is no reflected signal when the amplitude is very small.The phase of the surface reflection coefficient and the phase difference between the direct and reflected signals are searched as the same part,and this represents the multipath phase attenuation.The Cramer-Rao bound of the proposed algorithm is also derived.Finally,computer simulations and real data processing results show that the proposed algorithm has good estimation performance under complex scenarios and works well with only one snapshot.
文摘为了解决施工场景下安全帽佩戴检测时,由于人员密集、遮挡和复杂背景等原因造成的小目标漏检和错检的问题,提出一种基于YOLOv8n的双重注意力机制的跨层多尺度安全帽佩戴检测算法。首先,设计微小目标检测头,以提高模型对小目标的检测能力;其次,在特征提取网络中嵌入双重注意力机制,从而更加关注复杂场景下目标信息的特征捕获;然后,将特征融合网络替换成重参数化泛化特征金字塔网络(RepGFPN)改进后的跨层多尺度特征融合结构S-GFPN(Selective layer Generalized Feature Pyramid Network),以实现小目标特征层信息和其他特征层的多尺度融合,并建立长期的依赖关系,从而抑制背景信息的干扰;最后,采用MPDIOU(Intersection Over Union with Minimum Point Distance)损失函数来解决尺度变化不敏感的问题。在公开数据集GDUT-HWD上的实验结果表明,改进后的模型比YOLOv8n的mAP@0.5提升了3.4个百分点,对蓝色、黄色、白色和红色安全帽的检测精度分别提升了2.0、1.1、4.6和9.1个百分点,在密集、遮挡、小目标、反光和黑暗这5类复杂场景下的可视化检测效果也优于YOLOv8n,为实际施工场景中安全帽佩戴检测提供了一种有效方法。
基金supported by the National Shrimp and Crab Industry Technical System Construction Project 2022(No.CARS-48)the National Natural Science Foundation of China(No.62076244)+2 种基金the Chinese Universities Scientific Fund(No.2022TC109)the Double First-class Project of China Agricultural University(2022)and the Double First-class International Cooperation Project of China Agricultural University(No.10020799).
文摘The swift evolution of deep learning has greatly benefited the field of intensive aquaculture.Specifically,deep learning-based shrimp larvae detection has offered important technical assistance for counting shrimp larvae and recognizing abnormal behaviors.Firstly,the transparent bodies and small sizes of shrimp larvae,combined with complex scenarios due to variations in light intensity and water turbidity,make it challenging for current detection methods to achieve high accuracy.Secondly,deep learning-based object detection demands substantial computing power and storage space,which restricts its application on edge devices.This paper proposes an efficient one-stage shrimp larvae detection method,FAMDet,specifically designed for complex scenarios in intensive aquaculture.Firstly,different from the ordinary detection methods,it exploits an efficient FasterNet backbone,constructed with partial convolution,to extract effective multi-scale shrimp larvae features.Meanwhile,we construct an adaptively bi-directional fusion neck to integrate high-level semantic information and low-level detail information of shrimp larvae in a matter that sufficiently merges features and further mitigates noise interference.Finally,a decoupled detection head equipped with MPDIoU is used for precise bounding box regression of shrimp larvae.We collected images of shrimp larvae from multiple scenarios and labeled 108,365 targets for experiments.Compared with the ordinary detection methods(Faster RCNN,SSD,RetinaNet,CenterNet,FCOS,DETR,and YOLOX_s),FAMDet has obtained considerable advantages in accuracy,speed,and complexity.Compared with the outstanding one-stage method YOLOv8s,it has improved accuracy while reducing 57%parameters,37%FLOPs,22%inference latency per image on CPU,and 56%storage overhead.Furthermore,FAMDet has still outperformed multiple lightweight methods(EfficientDet,RT-DETR,GhostNetV2,EfficientFormerV2,EfficientViT,and MobileNetV4).In addition,we conducted experiments on the public dataset(VOC 07+12)to further verify the effectiveness of FAMDet.Consequently,the proposed method can effectively alleviate the limitations faced by resource-constrained devices and achieve superior shrimp larvae detection results.
