ArticleScientific reports2019
Whale counting in satellite and aerial images with deep learning.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed, 160 citations in OpenAlex.
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- Machine-Learning Approach for Automatic Detection of Wild Beluga Whales from Hand-Held Camera Pictures.Sensors (Basel, Switzerland) · 2022Article
- Whales from space dataset, an annotated satellite image dataset of whales for training machine learning models.Scientific data · 2022Article
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- Deep learning with self-supervision and uncertainty regularization to count fish in underwater images.PloS one · 2022Article
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- Automatic detection of fish and tracking of movement for ecology.Ecology and evolution · 2021Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite their interest and threat status, the number of whales in world's oceans remains highly uncertain. Whales detection is normally carried out from costly sighting surveys, acoustic surveys or through high-resolution images. Since deep convolutional neural networks (CNNs) are achieving great performance in several computer vision tasks, here we propose a robust and generalizable CNN-based system for automatically detecting and counting whales in satellite and aerial images based on open data and tools. In particular, we designed a two-step whale counting approach, where the first CNN finds the input images with whale presence, and the second CNN locates and counts each whale in those images. A test of the system on Google Earth images in ten global whale-watching hotspots achieved a performance (F1-measure) of 81% in detecting and 94% in counting whales. Combining these two steps increased accuracy by 36% compared to a baseline detection model alone. Applying this cost-effective method worldwide could contribute to the assessment of whale populations to guide conservation actions. Free and global access to high-resolution imagery for conservation purposes would boost this process.
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