Machine-Learning Approach for Automatic Detection of Wild Beluga Whales from Hand-Held Camera Pictures

A key aspect of ocean protection consists in estimating the abundance of marine mammal population density within their habitat, which is usually accomplished using visual inspection and cameras from line-transect ships, small boats, and aircraft. However, marine mammal observation through vessel sur...

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Published in:	Sensors (Basel, Switzerland) Vol. 22; no. 11; p. 4107
Main Authors:	Araújo, Voncarlos M., Shukla, Ankita, Chion, Clément, Gambs, Sébastien, Michaud, Robert
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 28.05.2022 MDPI
Subjects:	Algorithms Animals automatic object detection Automation beluga whale monitoring Cameras Datasets deep learning Dolphins & porpoises Estuaries Marine mammals Neural networks ocean protection Whales & whaling deep learning beluga whale monitoring automatic object detection ocean protection
ISSN:	1424-8220, 1424-8220
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Abstract	A key aspect of ocean protection consists in estimating the abundance of marine mammal population density within their habitat, which is usually accomplished using visual inspection and cameras from line-transect ships, small boats, and aircraft. However, marine mammal observation through vessel surveys requires significant workforce resources, including for the post-processing of pictures, and is further challenged due to animal bodies being partially hidden underwater, small-scale object size, occlusion among objects, and distracter objects (e.g., waves, sun glare, etc.). To relieve the human expert’s workload while improving the observation accuracy, we propose a novel system for automating the detection of beluga whales (Delphinapterus leucas) in the wild from pictures. Our system relies on a dataset named Beluga-5k, containing more than 5.5 thousand pictures of belugas. First, to improve the dataset’s annotation, we have designed a semi-manual strategy for annotating candidates in images with single (i.e., one beluga) and multiple (i.e., two or more belugas) candidate subjects efficiently. Second, we have studied the performance of three off-the-shelf object-detection algorithms, namely, Mask-RCNN, SSD, and YOLO v3-Tiny, on the Beluga-5k dataset. Afterward, we have set YOLO v3-Tiny as the detector, integrating single- and multiple-individual images into the model training. Our fine-tuned CNN-backbone detector trained with semi-manual annotations is able to detect belugas despite the presence of distracter objects with high accuracy (i.e., 97.05 mAP@0.5). Finally, our proposed method is able to detect overlapped/occluded multiple individuals in images (beluga whales that swim in groups). For instance, it is able to detect 688 out of 706 belugas encountered in 200 multiple images, achieving 98.29% precision and 99.14% recall.
AbstractList	A key aspect of ocean protection consists in estimating the abundance of marine mammal population density within their habitat, which is usually accomplished using visual inspection and cameras from line-transect ships, small boats, and aircraft. However, marine mammal observation through vessel surveys requires significant workforce resources, including for the post-processing of pictures, and is further challenged due to animal bodies being partially hidden underwater, small-scale object size, occlusion among objects, and distracter objects (e.g., waves, sun glare, etc.). To relieve the human expert’s workload while improving the observation accuracy, we propose a novel system for automating the detection of beluga whales (Delphinapterus leucas) in the wild from pictures. Our system relies on a dataset named Beluga-5k, containing more than 5.5 thousand pictures of belugas. First, to improve the dataset’s annotation, we have designed a semi-manual strategy for annotating candidates in images with single (i.e., one beluga) and multiple (i.e., two or more belugas) candidate subjects efficiently. Second, we have studied the performance of three off-the-shelf object-detection algorithms, namely, Mask-RCNN, SSD, and YOLO v3-Tiny, on the Beluga-5k dataset. Afterward, we have set YOLO v3-Tiny as the detector, integrating single- and multiple-individual images into the model training. Our fine-tuned CNN-backbone detector trained with semi-manual annotations is able to detect belugas despite the presence of distracter objects with high accuracy (i.e., 97.05 mAP@0.5). Finally, our proposed method is able to detect overlapped/occluded multiple individuals in images (beluga whales that swim in groups). For instance, it is able to detect 688 out of 706 belugas encountered in 200 multiple images, achieving 98.29% precision and 99.14% recall. A key aspect of ocean protection consists in estimating the abundance of marine mammal population density within their habitat, which is usually accomplished using visual inspection and cameras from line-transect ships, small boats, and aircraft. However, marine mammal observation through vessel surveys requires significant workforce resources, including for the post-processing of pictures, and is further challenged due to animal bodies being partially hidden underwater, small-scale object size, occlusion among objects, and distracter objects (e.g., waves, sun glare, etc.). To relieve the human expert's workload while improving the observation accuracy, we propose a novel system for automating the detection of beluga whales (Delphinapterus leucas) in the wild from pictures. Our system relies on a dataset named Beluga-5k, containing more than 5.5 thousand pictures of belugas. First, to improve the dataset's annotation, we have designed a semi-manual strategy for annotating candidates in images with single (i.e., one beluga) and multiple (i.e., two or more belugas) candidate subjects efficiently. Second, we have studied the performance of three off-the-shelf object-detection algorithms, namely, Mask-RCNN, SSD, and YOLO v3-Tiny, on the Beluga-5k dataset. Afterward, we have set YOLO v3-Tiny as the detector, integrating single- and multiple-individual images into the model training. Our fine-tuned CNN-backbone detector trained with semi-manual annotations is able to detect belugas despite the presence of distracter objects with high accuracy (i.e., 97.05 mAP@0.5). Finally, our proposed method is able to detect overlapped/occluded multiple individuals in images (beluga whales that swim in groups). For instance, it is able to detect 688 out of 706 belugas encountered in 200 multiple images, achieving 98.29% precision and 99.14% recall.A key aspect of ocean protection consists in estimating the abundance of marine mammal population density within their habitat, which is usually accomplished using visual inspection and cameras from line-transect ships, small boats, and aircraft. However, marine mammal observation through vessel surveys requires significant workforce resources, including for the post-processing of pictures, and is further challenged due to animal bodies being partially hidden underwater, small-scale object size, occlusion among objects, and distracter objects (e.g., waves, sun glare, etc.). To relieve the human expert's workload while improving the observation accuracy, we propose a novel system for automating the detection of beluga whales (Delphinapterus leucas) in the wild from pictures. Our system relies on a dataset named Beluga-5k, containing more than 5.5 thousand pictures of belugas. First, to improve the dataset's annotation, we have designed a semi-manual strategy for annotating candidates in images with single (i.e., one beluga) and multiple (i.e., two or more belugas) candidate subjects efficiently. Second, we have studied the performance of three off-the-shelf object-detection algorithms, namely, Mask-RCNN, SSD, and YOLO v3-Tiny, on the Beluga-5k dataset. Afterward, we have set YOLO v3-Tiny as the detector, integrating single- and multiple-individual images into the model training. Our fine-tuned CNN-backbone detector trained with semi-manual annotations is able to detect belugas despite the presence of distracter objects with high accuracy (i.e., 97.05 mAP@0.5). Finally, our proposed method is able to detect overlapped/occluded multiple individuals in images (beluga whales that swim in groups). For instance, it is able to detect 688 out of 706 belugas encountered in 200 multiple images, achieving 98.29% precision and 99.14% recall. A key aspect of ocean protection consists in estimating the abundance of marine mammal population density within their habitat, which is usually accomplished using visual inspection and cameras from line-transect ships, small boats, and aircraft. However, marine mammal observation through vessel surveys requires significant workforce resources, including for the post-processing of pictures, and is further challenged due to animal bodies being partially hidden underwater, small-scale object size, occlusion among objects, and distracter objects (e.g., waves, sun glare, etc.). To relieve the human expert's workload while improving the observation accuracy, we propose a novel system for automating the detection of beluga whales ( ) in the wild from pictures. Our system relies on a dataset named Beluga-5k, containing more than 5.5 thousand pictures of belugas. First, to improve the dataset's annotation, we have designed a semi-manual strategy for annotating candidates in images with single (i.e., one beluga) and multiple (i.e., two or more belugas) candidate subjects efficiently. Second, we have studied the performance of three off-the-shelf object-detection algorithms, namely, Mask-RCNN, SSD, and YOLO v3-Tiny, on the Beluga-5k dataset. Afterward, we have set YOLO v3-Tiny as the detector, integrating single- and multiple-individual images into the model training. Our fine-tuned CNN-backbone detector trained with semi-manual annotations is able to detect belugas despite the presence of distracter objects with high accuracy (i.e., 97.05 mAP@0.5). Finally, our proposed method is able to detect overlapped/occluded multiple individuals in images (beluga whales that swim in groups). For instance, it is able to detect 688 out of 706 belugas encountered in 200 multiple images, achieving 98.29% precision and 99.14% recall.
Author	Chion, Clément Araújo, Voncarlos M. Gambs, Sébastien Shukla, Ankita Michaud, Robert
AuthorAffiliation	3 Département d’Informatique, Université du Québec à Montréal (UQAM), Montreal, QC H2L 2C4, Canada; gambs.sebastien@uqam.ca 4 Groupe de Recherche et d’Éducation sur les Mammifères Marins (GREMM), Tadoussac, QC G0T 2A0, Canada; rmichaud@gremm.org 2 School of Arts, Media and Engineering, Arizona State University, Tempe, AZ 85281, USA; ashukl20@asu.edu 1 Département des Sciences Naturelles, Université du Québec en Outaouais, Ripon, QC J0V 1V0, Canada; clement.chion@uqo.ca
AuthorAffiliation_xml	– name: 2 School of Arts, Media and Engineering, Arizona State University, Tempe, AZ 85281, USA; ashukl20@asu.edu – name: 4 Groupe de Recherche et d’Éducation sur les Mammifères Marins (GREMM), Tadoussac, QC G0T 2A0, Canada; rmichaud@gremm.org – name: 1 Département des Sciences Naturelles, Université du Québec en Outaouais, Ripon, QC J0V 1V0, Canada; clement.chion@uqo.ca – name: 3 Département d’Informatique, Université du Québec à Montréal (UQAM), Montreal, QC H2L 2C4, Canada; gambs.sebastien@uqam.ca
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Cites_doi	10.1109/CVPR.2017.685 10.1038/s41598-019-50795-9 10.1071/AM14023 10.1007/978-3-319-10599-4 10.1371/journal.pone.0212532 10.1016/j.marpolbul.2015.08.045 10.1109/TNNLS.2018.2876865 10.3389/fmars.2018.00271 10.1016/j.ecoinf.2019.01.006 10.1093/bioinformatics/btz259 10.1109/CVPR.2016.90 10.1080/01431160600746456 10.1109/TPAMI.2016.2577031 10.1109/CVPR.2014.81 10.1111/j.1523-1739.2006.00338.x 10.1139/juvs-2021-0024 10.1007/978-3-030-30639-7_7 10.1111/mms.12141 10.1007/978-3-7908-2604-3 10.1016/S0964-5691(00)00028-4 10.1109/CVPR.2016.91 10.1109/WACVW.2015.10 10.1109/ICCV.2017.322 10.1109/ICCV.2015.169 10.3390/s20154276 10.1080/13880290701229838 10.1007/978-3-319-46448-0_2 10.1109/WACV.2017.105
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StartPage	4107
SubjectTerms	Algorithms Animals automatic object detection Automation beluga whale monitoring Cameras Datasets deep learning Dolphins & porpoises Estuaries Marine mammals Neural networks ocean protection Whales & whaling
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Title	Machine-Learning Approach for Automatic Detection of Wild Beluga Whales from Hand-Held Camera Pictures
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Volume	22
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