<p>Recently, Unmanned Aerial Vehicles (UAVs) equipped with high-resolution imaging sensors have become a viable alternative for ecologists to conduct wildlife censuses, compared to foot surveys. They cause less disturbance by sensing remotely, they provide coverage of otherwise inaccessible areas, and their images can be reviewed and double-checked in controlled screening sessions. However, the amount of data they generate often makes this photo-interpretation stage prohibitively time-consuming.</p><p>In this work, we automate the detection process with deep learning [4]. We focus on counting coastal seabirds on sand islands off the West African coast, where species like the African Royal Tern are at the top of the food chain [5]. Monitoring their abundance provides invaluable insights into biodiversity in this area [7]. In a first step, we obtained orthomosaics from nadir-looking UAVs over six sand islands with 1cm resolution. We then fully labelled one of them with points for four seabird species, which required three weeks for five annotators to do and resulted in over 21,000 individuals. Next, we further labelled the other five orthomosaics, but in an incomplete manner; we aimed for a low number of only 200 points per species. These points, together with a few background polygons, served as training data for our ResNet-based [2] detection model. This low number of points required multiple strategies to obtain stable predictions, including curriculum learning [1] and post-processing by a Markov random field [6]. In the end, our model was able to accurately predict the 21,000 birds of the test image with 90% precision at 90% recall (Fig. 1) [3]. Furthermore, this model required a mere 4.5 hours from creating training data to the final prediction, which is a fraction of the three weeks needed for the manual labelling process. Inference time is only a few minutes, which makes the model scale favourably to many more islands. In sum, the combination of UAVs and machine learning-based detectors simultaneously provides census possibilities with unprecedentedly high accuracy and comparably minuscule execution time.</p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.bc5211f4f60067568601161/sdaolpUECMynit/12UGE&app=m&a=0&c=eeda7238e992b9591c2fec19197f67dc&ct=x&pn=gnp.elif&d=1" alt=""></p><p><em>Fig. 1: Our model is able to predict over 21,000 birds in high-resolution UAV images in a fraction of time compared to weeks of manual labelling.</em></p><p>&#160;</p><p>References</p><p>1. Bengio, Yoshua, et al. "Curriculum learning." Proceedings of the 26th annual international conference on machine learning. 2009.</p><p>2. He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.</p><p>3. Kellenberger, Benjamin, et al. &#8220;21,000 Birds in 4.5 Hours: Efficient Large-scale Seabird Detection with Machine Learning.&#8221; Remote Sensing in Ecology and Conservation. Under review.</p><p>4. LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." nature 521.7553 (2015): 436-444.</p><p>5. Parsons, Matt, et al. "Seabirds as indicators of the marine environment." ICES Journal of Marine Science 65.8 (2008): 1520-1526.</p><p>6. Tuia, Devis, Michele Volpi, and Gabriele Moser. "Decision fusion with multiple spatial supports by conditional random fields." IEEE Transactions on Geoscience and Remote Sensing 56.6 (2018): 3277-3289.</p><p>7. Veen, Jan, Hanneke Dallmeijer, and Thor Veen. "Selecting piscivorous bird species for monitoring environmental change in the Banc d'Arguin, Mauritania." Ardea 106.1 (2018): 5-18.</p>
From Statistical Detection to Decision Fusion: Detection of Underwater Mines in High Resolution SAS Images