Individual Minke Whale Recognition Using Deep Learning Convolutional Neural Networks

Abstract

The only known predictable aggregation of dwarf minke whales (Balaenoptera acutorostrata subsp.) occurs in the Australian offshore waters of the northern Great Barrier Reef in May-August each year. The identification of individual whales is required for research on the whales' population characteristics and for monitoring the potential impacts of tourism activities, including commercial swims with the whales. At present, it is not cost-effective for researchers to manually process and analyze the tens of thousands of underwater images collated after each observation/tourist season, and a large data base of historical non-identified imagery exists. This study reports the first proof of concept for recognizing individual dwarf minke whales using the Deep Learning Convolutional Neural Networks (CNN).The "off-the-shelf" Image net-trained VGG16 CNN was used as the feature-encoder of the per-pixel sematic segmentation Automatic Minke Whale Recognizer (AMWR). The most frequently photographed whale in a sample of 76 individual whales (MW1020) was identified in 179 images out of the total 1320 images provided. Training and image augmentation procedures were developed to compensate for the small number of available images. The trained AMWR achieved 93% prediction accuracy on the testing subset of 36 positive/MW1020 and 228 negative/not-MW1020 images, where each negative image contained at least one of the other 75 whales. Furthermore on the test subset, AMWR achieved 74% precision, 80% recall, and 4% false-positive rate, making the presented approach comparable or better to other state-of-the-art individual animal recognition results.

Estimating Mass of Harvested Asian Seabass Lates calcarifer from Images

“…The recently developed semantic-segmentation Convolutional Neural Networks (CNN) [14] were highly successful in solving challenges where the segmentation of an image into per-pixel classes was required [11] [14] [15]. As discussed in the introduction, the second primary goal of this study was to design a practical Computer Vision algorithm to extract fish-body area from images.…”

Automatic Sorting of Dwarf Minke Whale Underwater Images

“…Somewhat like object-detection CNNs, semantic segmentation CNNs [33] could also be used to localise dwarf minke whales [10]. For example, in Reference [10], 100 segmentation masks were prepared manually.…”

“…Somewhat like object-detection CNNs, semantic segmentation CNNs [33] could also be used to localise dwarf minke whales [10]. For example, in Reference [10], 100 segmentation masks were prepared manually. However, such a small number of masks could only teach FCN-8s CNN [33] to localise whales within simple monotonic surroundings such as the example in Figure 1.…”

“…The whales' natural colour patterns are complex (especially around their head and shoulder areas), individually variable, and likely remain stable through a whale's life, enabling photo-identification (photo-ID) of individual animals [7][8][9][10], see a typical example in Figure 1. The identification of individual whales from digital imagery underpins research on population characteristics, biology and behaviour [6].…”

Common and Antarctic Minke Whales: Conservation Status and Future Research Directions

“…Due to an increasing quantity of images collected annually, the manual image matching process is not cost-effective for some larger populations, and new pattern recognition algorithms may provide a solution to this problem. Konovalov et al (2018) report on a "proof of concept" for recognizing individual dwarf minke whales using the Deep Learning Convolutional Neural Networks tool. Further advancements in the use of such technology may lead to a viable alternative to manual photo analysis in the near future.…”