Infrared small target detection is a common task in infrared image processing.Under limited computa⁃tional resources.Traditional methods for infrared small target detection face a trade-off between the detection rate ...Infrared small target detection is a common task in infrared image processing.Under limited computa⁃tional resources.Traditional methods for infrared small target detection face a trade-off between the detection rate and the accuracy.A fast infrared small target detection method tailored for resource-constrained conditions is pro⁃posed for the YOLOv5s model.This method introduces an additional small target detection head and replaces the original Intersection over Union(IoU)metric with Normalized Wasserstein Distance(NWD),while considering both the detection accuracy and the detection speed of infrared small targets.Experimental results demonstrate that the proposed algorithm achieves a maximum effective detection speed of 95 FPS on a 15 W TPU,while reach⁃ing a maximum effective detection accuracy of 91.9 AP@0.5,effectively improving the efficiency of infrared small target detection under resource-constrained conditions.展开更多
It is known that detecting small moving objects in as- tronomical image sequences is a significant research problem in space surveillance. The new theory, compressive sensing, pro- vides a very easy and computationall...It is known that detecting small moving objects in as- tronomical image sequences is a significant research problem in space surveillance. The new theory, compressive sensing, pro- vides a very easy and computationally cheap coding scheme for onboard astronomical remote sensing. An algorithm for small moving space object detection and localization is proposed. The algorithm determines the measurements of objects by comparing the difference between the measurements of the current image and the measurements of the background scene. In contrast to reconstruct the whole image, only a foreground image is recon- structed, which will lead to an effective computational performance, and a high level of localization accuracy is achieved. Experiments and analysis are provided to show the performance of the pro- posed approach on detection and localization.展开更多
针对菌落图像中小菌落易漏检的问题,提出了一种基于INC4-YOLO(you only look once)的计数方法,实现精准的菌落计数。采用带残差结构的Inception模块(Inception module with residual connection,IncRes)替换YOLOv5骨干网络中的Bottlenec...针对菌落图像中小菌落易漏检的问题,提出了一种基于INC4-YOLO(you only look once)的计数方法,实现精准的菌落计数。采用带残差结构的Inception模块(Inception module with residual connection,IncRes)替换YOLOv5骨干网络中的Bottleneck模块,以增强图像特征提取能力。从网络的浅层特征中引出一个小目标检测头,以增强算法在训练过程中对小菌落的注意力。分别在标注微生物自动识别数据集(annotated germs for automated recognition,AGAR)和真实菌落计数场景下对INC4-YOLO进行计数性能测试。实验结果表明,在AGAR测试集中,提出的算法在小菌落的平均百分比绝对值计数误差(mean absolute percentage error,MAPE)比其他先进目标检测算法降低了2%;真实菌落计数场景下,INC4-YOLO的MAPE相比YOLOv5降低了7%,表明该算法可帮助菌落计数设备实现精准计数。展开更多
文摘Infrared small target detection is a common task in infrared image processing.Under limited computa⁃tional resources.Traditional methods for infrared small target detection face a trade-off between the detection rate and the accuracy.A fast infrared small target detection method tailored for resource-constrained conditions is pro⁃posed for the YOLOv5s model.This method introduces an additional small target detection head and replaces the original Intersection over Union(IoU)metric with Normalized Wasserstein Distance(NWD),while considering both the detection accuracy and the detection speed of infrared small targets.Experimental results demonstrate that the proposed algorithm achieves a maximum effective detection speed of 95 FPS on a 15 W TPU,while reach⁃ing a maximum effective detection accuracy of 91.9 AP@0.5,effectively improving the efficiency of infrared small target detection under resource-constrained conditions.
基金supported by the National Natural Science Foundation of China (60903126)the China Postdoctoral Special Science Foundation (201003685)+1 种基金the China Postdoctoral Science Foundation (20090451397)the Northwestern Polytechnical University Foundation for Fundamental Research (JC201120)
文摘It is known that detecting small moving objects in as- tronomical image sequences is a significant research problem in space surveillance. The new theory, compressive sensing, pro- vides a very easy and computationally cheap coding scheme for onboard astronomical remote sensing. An algorithm for small moving space object detection and localization is proposed. The algorithm determines the measurements of objects by comparing the difference between the measurements of the current image and the measurements of the background scene. In contrast to reconstruct the whole image, only a foreground image is recon- structed, which will lead to an effective computational performance, and a high level of localization accuracy is achieved. Experiments and analysis are provided to show the performance of the pro- posed approach on detection and localization.
文摘针对菌落图像中小菌落易漏检的问题,提出了一种基于INC4-YOLO(you only look once)的计数方法,实现精准的菌落计数。采用带残差结构的Inception模块(Inception module with residual connection,IncRes)替换YOLOv5骨干网络中的Bottleneck模块,以增强图像特征提取能力。从网络的浅层特征中引出一个小目标检测头,以增强算法在训练过程中对小菌落的注意力。分别在标注微生物自动识别数据集(annotated germs for automated recognition,AGAR)和真实菌落计数场景下对INC4-YOLO进行计数性能测试。实验结果表明,在AGAR测试集中,提出的算法在小菌落的平均百分比绝对值计数误差(mean absolute percentage error,MAPE)比其他先进目标检测算法降低了2%;真实菌落计数场景下,INC4-YOLO的MAPE相比YOLOv5降低了7%,表明该算法可帮助菌落计数设备实现精准计数。
