CAMOUFLAGED objects are difficult to detect, for both humans and artificial intelligence. But now an AI has been trained to parse objects from their backgrounds.
This could have a variety of applications, such as being used for search-and-rescue work, detecting agricultural pests, medical imaging or in military settings.
Detecting camouflaged objects requires visual perception and knowledge. Until now, many AIs have struggled with this task because their algorithms rely on visual cues, such as differences in colour or easily recognisable shapes, to identify objects.
To improve on this, Jianbing Shen at the Inception Institute of Artificial Intelligence in Abu Dhabi in the United Arab Emirates and his colleagues collated a data set of 10,000 photographs to train an AI. The data set includes 5066 images of camouflaged objects, which they have divided into 78 categories, such as “amphibian”, “aquatic” and “flying”.
The photographs included both naturally camouflaged animals such as fish and insects and examples of artificial camouflage, such as soldiers in uniform. Although databases of camouflaged objects already exist, this data set is the largest, says Shen.
The team manually labelled each image of a camouflaged object to highlight characteristics such as its shape or whether it was partially obstructed by its surrounding environment.
They then developed an AI called SINet and trained it on images from the data set. The researchers compared SINet to 12 existing algorithms built to detect generic objects. They tested all 13 algorithms using three existing data sets of camouflaged objects. SINet
“ Many AIs struggle to detect camouflaged objects because their algorithms rely on visual cues”
did better than the other 12 at isolating camouflaged objects and identifying their correct shape and nature in both the existing and the training data sets.
“Without any bells and whistles, SINet outperforms various stateof-the-art object detection baselines on all datasets tested, making it a robust, general framework that can help facilitate future research,” the researchers write. They are due to present the work at the CVPR 2020 conference in Seattle, Washington, in June.
The researchers hope the data set and algorithm can improve AI’s ability to recognise camouflaged objects, says Shen. ❚
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