A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Deep Learning Based Multi-Modal Fusion Architectures for Maritime Vessel Detection
Tekijät: Fahimeh Farahnakian, Jukka Heikkonen
Kustantaja: MDPI
Julkaisuvuosi: 2020
Journal: Remote Sensing
Tietokannassa oleva lehden nimi: REMOTE SENSING
Lehden akronyymi: REMOTE SENS-BASEL
Artikkelin numero: ARTN 2509
Vuosikerta: 12
Numero: 6
Sivujen määrä: 17
eISSN: 2072-4292
DOI: https://doi.org/10.3390/rs12162509
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/50340179
Tiivistelmä
Object detection is a fundamental computer vision task for many real-world applications. In the maritime environment, this task is challenging due to varying light, view distances, weather conditions, and sea waves. In addition, light reflection, camera motion and illumination changes may cause to false detections. To address this challenge, we present three fusion architectures to fuse two imaging modalities: visible and infrared. These architectures can provide complementary information from two modalities in different levels: pixel-level, feature-level, and decision-level. They employed deep learning for performing fusion and detection. We investigate the performance of the proposed architectures conducting a real marine image dataset, which is captured by color and infrared cameras on-board a vessel in the Finnish archipelago. The cameras are employed for developing autonomous ships, and collect data in a range of operation and climatic conditions. Experiments show that feature-level fusion architecture outperforms the state-of-the-art other fusion level architectures.
Object detection is a fundamental computer vision task for many real-world applications. In the maritime environment, this task is challenging due to varying light, view distances, weather conditions, and sea waves. In addition, light reflection, camera motion and illumination changes may cause to false detections. To address this challenge, we present three fusion architectures to fuse two imaging modalities: visible and infrared. These architectures can provide complementary information from two modalities in different levels: pixel-level, feature-level, and decision-level. They employed deep learning for performing fusion and detection. We investigate the performance of the proposed architectures conducting a real marine image dataset, which is captured by color and infrared cameras on-board a vessel in the Finnish archipelago. The cameras are employed for developing autonomous ships, and collect data in a range of operation and climatic conditions. Experiments show that feature-level fusion architecture outperforms the state-of-the-art other fusion level architectures.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
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