In ocean explorations,side-scan sonar(SSS)plays a very important role and can quickly depict seabed topography.As-sembling the SSS to an autonomous underwater vehicle(AUV)and performing semantic segmentation of an SSS...In ocean explorations,side-scan sonar(SSS)plays a very important role and can quickly depict seabed topography.As-sembling the SSS to an autonomous underwater vehicle(AUV)and performing semantic segmentation of an SSS image in real time can realize online submarine geomorphology or target recognition,which is conducive to submarine detection.However,because of the complexity of the marine environment,various noises in the ocean pollute the sonar image,which also encounters the intensity inhomogeneity problem.In this paper,we propose a novel neural network architecture named dilated convolutional neural network(DcNet)that can run in real time while addressing the above-mentioned issues and providing accurate semantic segmentation.The proposed architecture presents an encoder-decoder network to gradually reduce the spatial dimension of the input image and recover the details of the target,respectively.The core of our network is a novel block connection named DCblock,which mainly uses dilated convolution and depthwise separable convolution between the encoder and decoder to attain more context while still retaining high accuracy.Furthermore,our proposed method performs a super-resolution reconstruction to enlarge the dataset with high-quality im-ages.We compared our network to other common semantic segmentation networks performed on an NVIDIA Jetson TX2 using our sonar image datasets.Experimental results show that while the inference speed of the proposed network significantly outperforms state-of-the-art architectures,the accuracy of our method is still comparable,which indicates its potential applications not only in AUVs equipped with SSS but also in marine exploration.展开更多
文摘针对Oligo(d T)亲和层析介质的吸附性能,以poly(A)为模型分子,考察了4种Oligo(d T)亲和层析介质的静态吸附平衡、吸附动力学和动态结合载量(DBC),探讨了载量影响相关机制。结果表明,4种介质的合适吸附条件均为0.6 mol·L-1Na Cl、p H=6~7;Monomix d T20静态吸附容量最大,且poly(A)能扩散至介质微球深层孔内,而Poros Oligo(d T)25、Praesto Jetted (d T)25和Nano Gel d T20等3种介质中poly(A)均主要为表层吸附、静态吸附容量稍低;对于DBC,Nano Gel d T20和Monomix d T20的10%穿透的DBC较高,而Poros Oligo (d T)25和Praesto Jetted (d T)25相对略低。经分析,影响载量的主要因素包含基质种类、微球孔径、配基密度、间隔臂和配基长度等。对于基质种类,聚苯乙烯基质可能孔道结构较为特别。对于微球孔径,应针对不同大小的m RNA分子定制不同孔径的微球,以平衡传质阻力与可及吸附表面积之间的矛盾,从而增大DBC。
基金This work is partially supported by the Natural Key Research and Development Program of China(No.2016YF C0301400).
文摘In ocean explorations,side-scan sonar(SSS)plays a very important role and can quickly depict seabed topography.As-sembling the SSS to an autonomous underwater vehicle(AUV)and performing semantic segmentation of an SSS image in real time can realize online submarine geomorphology or target recognition,which is conducive to submarine detection.However,because of the complexity of the marine environment,various noises in the ocean pollute the sonar image,which also encounters the intensity inhomogeneity problem.In this paper,we propose a novel neural network architecture named dilated convolutional neural network(DcNet)that can run in real time while addressing the above-mentioned issues and providing accurate semantic segmentation.The proposed architecture presents an encoder-decoder network to gradually reduce the spatial dimension of the input image and recover the details of the target,respectively.The core of our network is a novel block connection named DCblock,which mainly uses dilated convolution and depthwise separable convolution between the encoder and decoder to attain more context while still retaining high accuracy.Furthermore,our proposed method performs a super-resolution reconstruction to enlarge the dataset with high-quality im-ages.We compared our network to other common semantic segmentation networks performed on an NVIDIA Jetson TX2 using our sonar image datasets.Experimental results show that while the inference speed of the proposed network significantly outperforms state-of-the-art architectures,the accuracy of our method is still comparable,which indicates its potential applications not only in AUVs equipped with SSS but also in marine exploration.
