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Pregled bibliografske jedinice broj: 1077879

Comparison of two deep learning methods for ship target recognition with optical remotely sensed data


Dianjun Zhang; Jie Zhan; Lifeng Tan; Yuhang Gao; Robert Župan
Comparison of two deep learning methods for ship target recognition with optical remotely sensed data // Neural Computing and Applications, 1 (2020), 1; 1-11 doi:10.1007/s00521-020-05307-6 (međunarodna recenzija, članak, znanstveni)


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Naslov
Comparison of two deep learning methods for ship target recognition with optical remotely sensed data

Autori
Dianjun Zhang ; Jie Zhan ; Lifeng Tan ; Yuhang Gao ; Robert Župan

Izvornik
Neural Computing and Applications (0941-0643) 1 (2020), 1; 1-11

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Full convolutional network, Ship target recognition, Pixel level, Mask R-CNN, Faster R-CNN, Optical remote sensing images

Sažetak
As an important part of modern marine monitoring systems, ship target identification has important significance inmaintaining marine rights and monitoring maritime traffic. With the development of artificial intelligence technology, image detection and recognition based on deep learning methods have become the most popular and practical method. Inthis paper, two deep learning algorithms, the Mask R-CNN algorithm and the Faster R-CNN algorithm, are used to buildship target feature extraction and recognition models based on deep convolutional neural networks. The established modelswere compared and analyzed to verify the feasibility of target detection algorithms. In this study, 5748 remote sensingmaps were selected as the dataset for experiments, and two algorithms were used to classify and extract warships andcivilian ships. Experiments showed that for the accuracy of ship identification, Mask R-CNN and Faster R-CNN reached95.21% and 92.76%, respectively. These results demonstrated that the Mask R-CNN algorithm achieves pixel-levelsegmentation. Compared with the Faster R-CNN algorithm, the obtained target detection effect is more accurate, and theperformance in target detection and classification is better, which reflects the great advantage of pixel-level recognition.

Izvorni jezik
Engleski

Znanstvena područja
Geodezija



POVEZANOST RADA


Ustanove:
Geodetski fakultet, Zagreb

Profili:

Avatar Url Robert Župan (autor)

Citiraj ovu publikaciju

Dianjun Zhang; Jie Zhan; Lifeng Tan; Yuhang Gao; Robert Župan
Comparison of two deep learning methods for ship target recognition with optical remotely sensed data // Neural Computing and Applications, 1 (2020), 1; 1-11 doi:10.1007/s00521-020-05307-6 (međunarodna recenzija, članak, znanstveni)
Dianjun Zhang, Jie Zhan, Lifeng Tan, Yuhang Gao & Robert Župan (2020) Comparison of two deep learning methods for ship target recognition with optical remotely sensed data. Neural Computing and Applications, 1 (1), 1-11 doi:10.1007/s00521-020-05307-6.
@article{article, year = {2020}, pages = {1-11}, DOI = {10.1007/s00521-020-05307-6}, keywords = {Full convolutional network, Ship target recognition, Pixel level, Mask R-CNN, Faster R-CNN, Optical remote sensing images}, journal = {Neural Computing and Applications}, doi = {10.1007/s00521-020-05307-6}, volume = {1}, number = {1}, issn = {0941-0643}, title = {Comparison of two deep learning methods for ship target recognition with optical remotely sensed data}, keyword = {Full convolutional network, Ship target recognition, Pixel level, Mask R-CNN, Faster R-CNN, Optical remote sensing images} }
@article{article, year = {2020}, pages = {1-11}, DOI = {10.1007/s00521-020-05307-6}, keywords = {Full convolutional network, Ship target recognition, Pixel level, Mask R-CNN, Faster R-CNN, Optical remote sensing images}, journal = {Neural Computing and Applications}, doi = {10.1007/s00521-020-05307-6}, volume = {1}, number = {1}, issn = {0941-0643}, title = {Comparison of two deep learning methods for ship target recognition with optical remotely sensed data}, keyword = {Full convolutional network, Ship target recognition, Pixel level, Mask R-CNN, Faster R-CNN, Optical remote sensing images} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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