Instance segmentation plays an important role in image processing.The Deep Snake algorithm based on contour iteration deforms an initial bounding box to an instance contour end-to-end,which can improve the performance...Instance segmentation plays an important role in image processing.The Deep Snake algorithm based on contour iteration deforms an initial bounding box to an instance contour end-to-end,which can improve the performance of instance segmentation,but has defects such as slow segmentation speed and sub-optimal initial contour.To solve these problems,a real-time instance segmentation algorithm based on contour learning was proposed.Firstly,ShuffleNet V2 was used as backbone network,and the receptive field of the model was expanded by using a 5×5 convolution kernel.Secondly,a lightweight up-sampling module,multi-stage aggregation(MSA),performs residual fusion of multi-layer features,which not only improves segmentation speed,but also extracts effective features more comprehensively.Thirdly,a contour initialization method for network learning was designed,and a global contour feature aggregation mechanism was used to return a coarse contour,which solves the problem of excessive error between manually initialized contour and real contour.Finally,the Snake deformation module was used to iteratively optimize the coarse contour to obtain the final instance contour.The experimental results showed that the proposed method improved the instance segmentation accuracy on semantic boundaries dataset(SBD),Cityscapes and Kins datasets,and the average precision reached 55.8 on the SBD;Compared with Deep Snake,the model parameters were reduced by 87.2%,calculation amount was reduced by 78.3%,and segmentation speed reached 39.8 frame·s−1 when instance segmentation was performed on an image with a size of 512×512 pixels on a 2080Ti GPU.The proposed method can reduce resource consumption,realize instance segmentation tasks quickly and accurately,and therefore is more suitable for embedded platforms with limited resources.展开更多
为了解决金融FAST(financial information exchange adapted for streaming)协议面临的纯软件解码延迟高,FPGA(field programmable gate array)硬件解码开发周期长、更新困难的问题,提出了基于OpenCL和HLS的硬件解码模式。通过对FAST数...为了解决金融FAST(financial information exchange adapted for streaming)协议面临的纯软件解码延迟高,FPGA(field programmable gate array)硬件解码开发周期长、更新困难的问题,提出了基于OpenCL和HLS的硬件解码模式。通过对FAST数据解码的标记、切分、合并、解码模块进行流水优化,对切分和字段解码进行并行操作,将数据的输入输出改为流式接口减少I/O口的延时以及对切分数组进行分割映射等优化方式实现了解码过程低延迟、低抖动。实验结果表明,相比纯软件解码,本文提出的解码器处理速度提升了11倍,解码延迟缩短至1/6,抖动幅度控制在10 ns之内。相比传统HDL方式的FPGA定制硬件开发,开发效率可提升3~4倍,从而更好地满足产品更新换代的需求。展开更多
基金supported by National Key Research and Development Program(No.2022YFE0112400)National Natural Science Foundation of China(No.21706096)Natural Science Foundation of Jiangsu Province(No.BK20160162).
文摘Instance segmentation plays an important role in image processing.The Deep Snake algorithm based on contour iteration deforms an initial bounding box to an instance contour end-to-end,which can improve the performance of instance segmentation,but has defects such as slow segmentation speed and sub-optimal initial contour.To solve these problems,a real-time instance segmentation algorithm based on contour learning was proposed.Firstly,ShuffleNet V2 was used as backbone network,and the receptive field of the model was expanded by using a 5×5 convolution kernel.Secondly,a lightweight up-sampling module,multi-stage aggregation(MSA),performs residual fusion of multi-layer features,which not only improves segmentation speed,but also extracts effective features more comprehensively.Thirdly,a contour initialization method for network learning was designed,and a global contour feature aggregation mechanism was used to return a coarse contour,which solves the problem of excessive error between manually initialized contour and real contour.Finally,the Snake deformation module was used to iteratively optimize the coarse contour to obtain the final instance contour.The experimental results showed that the proposed method improved the instance segmentation accuracy on semantic boundaries dataset(SBD),Cityscapes and Kins datasets,and the average precision reached 55.8 on the SBD;Compared with Deep Snake,the model parameters were reduced by 87.2%,calculation amount was reduced by 78.3%,and segmentation speed reached 39.8 frame·s−1 when instance segmentation was performed on an image with a size of 512×512 pixels on a 2080Ti GPU.The proposed method can reduce resource consumption,realize instance segmentation tasks quickly and accurately,and therefore is more suitable for embedded platforms with limited resources.
文摘为了解决金融FAST(financial information exchange adapted for streaming)协议面临的纯软件解码延迟高,FPGA(field programmable gate array)硬件解码开发周期长、更新困难的问题,提出了基于OpenCL和HLS的硬件解码模式。通过对FAST数据解码的标记、切分、合并、解码模块进行流水优化,对切分和字段解码进行并行操作,将数据的输入输出改为流式接口减少I/O口的延时以及对切分数组进行分割映射等优化方式实现了解码过程低延迟、低抖动。实验结果表明,相比纯软件解码,本文提出的解码器处理速度提升了11倍,解码延迟缩短至1/6,抖动幅度控制在10 ns之内。相比传统HDL方式的FPGA定制硬件开发,开发效率可提升3~4倍,从而更好地满足产品更新换代的需求。
