Seabed sediment recognition is vital for the exploitation of marine resources.Side-scan sonar(SSS)is an excellent tool for acquiring the imagery of seafloor topography.Combined with ocean surface sampling,it provides ...Seabed sediment recognition is vital for the exploitation of marine resources.Side-scan sonar(SSS)is an excellent tool for acquiring the imagery of seafloor topography.Combined with ocean surface sampling,it provides detailed and accurate images of marine substrate features.Most of the processing of SSS imagery works around limited sampling stations and requires manual interpretation to complete the classification of seabed sediment imagery.In complex sea areas,with manual interpretation,small targets are often lost due to a large amount of information.To date,studies related to the automatic recognition of seabed sediments are still few.This paper proposes a seabed sediment recognition method based on You Only Look Once version 5 and SSS imagery to perform real-time sedi-ment classification and localization for accuracy,particularly on small targets and faster speeds.We used methods such as changing the dataset size,epoch,and optimizer and adding multiscale training to overcome the challenges of having a small sample and a low accuracy.With these methods,we improved the results on mean average precision by 8.98%and F1 score by 11.12%compared with the original method.In addition,the detection speed was approximately 100 frames per second,which is faster than that of previous methods.This speed enabled us to achieve real-time seabed sediment recognition from SSS imagery.展开更多
Spontaneous growth of A-element whiskers on Mn+1AXn(MAX for short) phase materials poses a barrier to their practical applications, since it casts doubts on their stability. In this study, Ga whisker growth on sintere...Spontaneous growth of A-element whiskers on Mn+1AXn(MAX for short) phase materials poses a barrier to their practical applications, since it casts doubts on their stability. In this study, Ga whisker growth on sintered Cr2GaC samples was investigated. The elemental source for spontaneous growth of Ga whiskers is identified as the free Ga contained in the Cr2GaC material, not the lattice atoms from Cr2GaC grains, which removes the doubts on the stability of Cr2GaC material. The growth behavior and morphologies of the Ga whiskers follow a new catalysis-based model, with cleavage planes of Cr2GaC grains involved as nucleation sites. This model explains and predicts well the growth behavior of the whiskers. The mitigation strategy based on this model is in principle simple: to prevent free Ga in Cr2GaC material or limit it to a certain level;to avoid cleavage plane of Cr2GaC grains;to achieve high density of the Cr2GaC material.展开更多
基金funded by the Natural Science Foundation of Fujian Province(No.2018J01063)the Project of Deep Learning Based Underwater Cultural Relics Recognization(No.38360041)the Project of the State Administration of Cultural Relics(No.2018300).
文摘Seabed sediment recognition is vital for the exploitation of marine resources.Side-scan sonar(SSS)is an excellent tool for acquiring the imagery of seafloor topography.Combined with ocean surface sampling,it provides detailed and accurate images of marine substrate features.Most of the processing of SSS imagery works around limited sampling stations and requires manual interpretation to complete the classification of seabed sediment imagery.In complex sea areas,with manual interpretation,small targets are often lost due to a large amount of information.To date,studies related to the automatic recognition of seabed sediments are still few.This paper proposes a seabed sediment recognition method based on You Only Look Once version 5 and SSS imagery to perform real-time sedi-ment classification and localization for accuracy,particularly on small targets and faster speeds.We used methods such as changing the dataset size,epoch,and optimizer and adding multiscale training to overcome the challenges of having a small sample and a low accuracy.With these methods,we improved the results on mean average precision by 8.98%and F1 score by 11.12%compared with the original method.In addition,the detection speed was approximately 100 frames per second,which is faster than that of previous methods.This speed enabled us to achieve real-time seabed sediment recognition from SSS imagery.
基金National Natural Science Foundation of China(51731004,52101064,52072003)Anhui Provincial Natural Science Foundation(2008085QE195)+2 种基金National Key Research and Development Program of China(2017YFE0301403)Jiangsu Planned Projects for Postdoctoral Research Funds(2020Z158)Natural Science Foundation of Jiangsu Province(BK20201283)。
基金National Natural Science Foundation of China(51731004,51671054)Fundamental Research Funds for the Central Universities in China(2242018K40108,2242018K40109)+1 种基金Natural Science Foundation of Jiangsu Province(BK20181285)Youth Research Fund Project of Anhui University of Technology。
基金supported by the National Natural Science Foundation of China(Grant Nos.51501038&51731004)the Fundamental Research Funds for the Central Universities(Grant No.2242018K40109)
文摘Spontaneous growth of A-element whiskers on Mn+1AXn(MAX for short) phase materials poses a barrier to their practical applications, since it casts doubts on their stability. In this study, Ga whisker growth on sintered Cr2GaC samples was investigated. The elemental source for spontaneous growth of Ga whiskers is identified as the free Ga contained in the Cr2GaC material, not the lattice atoms from Cr2GaC grains, which removes the doubts on the stability of Cr2GaC material. The growth behavior and morphologies of the Ga whiskers follow a new catalysis-based model, with cleavage planes of Cr2GaC grains involved as nucleation sites. This model explains and predicts well the growth behavior of the whiskers. The mitigation strategy based on this model is in principle simple: to prevent free Ga in Cr2GaC material or limit it to a certain level;to avoid cleavage plane of Cr2GaC grains;to achieve high density of the Cr2GaC material.
