Abstract
| Original language | English |
|---|---|
| Book series | I F A C Workshop Series |
| Volume | 52 |
| Issue number | 21 |
| Pages (from-to) | 64-71 |
| ISSN | 1474-6670 |
| DOIs | |
| Publication status | Published - 2019 |
| Event | 12th Control Applications in Marine Systems, Robotics, and Vehicles - Chunghi & Byiung Jun Park KI Building, KAIST, Daejeon, Korea, Republic of Duration: 18 Sep 2019 → 20 Sep 2019 Conference number: 12 http://cams-wroco.org/ |
Conference
| Conference | 12th Control Applications in Marine Systems, Robotics, and Vehicles |
|---|---|
| Number | 12 |
| Location | Chunghi & Byiung Jun Park KI Building, KAIST |
| Country | Korea, Republic of |
| City | Daejeon |
| Period | 18/09/2019 → 20/09/2019 |
| Internet address |
Keywords
- Object Detection
- Autonomous marine crafts
- Navigation
- Long-wave Infra-red
- Detection at sea
- Autonomous Ship
Cite this
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Assessing Deep-learning Methods for Object Detection at Sea from LWIR Images. / Schöller, Frederik Emil Thorsson; Plenge-Feidenhans'l, Martin Krarup; Stets, Jonathan Dyssel; Blanke, Mogens.
In: I F A C Workshop Series, Vol. 52, No. 21, 2019, p. 64-71.Research output: Contribution to journal › Journal article › Research › peer-review
TY - JOUR
T1 - Assessing Deep-learning Methods for Object Detection at Sea from LWIR Images
AU - Schöller, Frederik Emil Thorsson
AU - Plenge-Feidenhans'l, Martin Krarup
AU - Stets, Jonathan Dyssel
AU - Blanke, Mogens
PY - 2019
Y1 - 2019
N2 - This paper assesses the performance of three convolutional neural networks for object detection at sea using Long Wavelength Infrared (LWIR) images in the 8 − 14µm range. Capturing images from ferries and annotating 20k images, fine-tuning is done of three state of art deep neural networks: RetinaNet, YOLO and Faster R-CNN. Targeting on vessels and buoys as two main classes of interest for navigation, performance is quantified by the cardinality of true and false positives and negatives in a random validation set. Calculating precision and recall as functions of tuning parameters for the three classifiers, noticeable differences are found between the three networks when used for LWIR image object classification at sea. The results lead to conclusions on imaging requirements when classification is used to support navigation.
AB - This paper assesses the performance of three convolutional neural networks for object detection at sea using Long Wavelength Infrared (LWIR) images in the 8 − 14µm range. Capturing images from ferries and annotating 20k images, fine-tuning is done of three state of art deep neural networks: RetinaNet, YOLO and Faster R-CNN. Targeting on vessels and buoys as two main classes of interest for navigation, performance is quantified by the cardinality of true and false positives and negatives in a random validation set. Calculating precision and recall as functions of tuning parameters for the three classifiers, noticeable differences are found between the three networks when used for LWIR image object classification at sea. The results lead to conclusions on imaging requirements when classification is used to support navigation.
KW - Object Detection
KW - Autonomous marine crafts
KW - Navigation
KW - Long-wave Infra-red
KW - Detection at sea
KW - Autonomous Ship
U2 - 10.1016/j.ifacol.2019.12.284
DO - 10.1016/j.ifacol.2019.12.284
M3 - Journal article
VL - 52
SP - 64
EP - 71
JO - I F A C Workshop Series
JF - I F A C Workshop Series
SN - 1474-6670
IS - 21
ER -