Marine Vertebrate Predator Detection and Recognition in Underwater Videos by Region Convolutional Neural Network
- Publisher:
- Springer International Publishing
- Publication Type:
- Conference Proceeding
- Citation:
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, 11669 LNAI, pp. 66-80
- Issue Date:
- 2019-01-01
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Park2019_Chapter_MarineVertebratePredatorDetect (1).pdf | Published version | 1.07 MB |
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© 2019, Springer Nature Switzerland AG. In this paper, we present R-CNN, Fast R-CNN and Faster R-CNN methods to automatically detect and recognise the predators in underwater videos. We compare the results of these methods on real data and discuss their strengths and weaknesses. We build a dataset using footage captured from representative environment of the wild and devise a data model with three classes (seal, dolphin, background). Following this, we train R-CNN, Fast R-CNN and Faster R-CNN, then evaluate them on a test dataset compose of challenging objects that had not been seen during training. We perform evaluation on GPU, acquiring information about the AP and IOU for each model and network based on various proposal numbers as well as runtime speeds. Based on the results, we found that the best model of predator detection using visual deep learning models is Faster R-CNN with 2000 proposals.
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