Abstract
Wild animals are essential for ecosystem structuring and stability, and thus they are important for ecological research. Since most wild animals have high athletic or concealable abilities or both, it is used to be relatively difficult to acquire evidence of animal appearances before applications of camera traps in ecological researches. However, a single camera trap may produce thousands of animal images in a short period of time and inevitably ends up with millions of images requiring classification. Although there have been many methods developed for classifying camera trap images, almost all of them follow the pattern of a very deep convolutional neural network processing all camera trap images. Consequently, the corresponding surveillance area may need to be delicately controlled to match the network capability, and it may be difficult to expand the area in the future. In this study, we consider a scenario in which camera traps are grouped into independent clusters, and images produced by a cluster are processed by an edge device installed with a customized network. Accordingly, edge devices in this scenario may be highly heterogeneous due to cluster scales. Resultantly, networks popular in the classification of camera trap images may not be deployable for edge devices without modifications requiring the expertise which may be hard to obtain. This motivates us to automatize network design via neural architecture search for edge devices. However, the search may be costly due to the evaluations of candidate networks, and its results may be infeasible without considering the resource limits of edge devices. Accordingly, we propose a search method using regression trees to evaluate candidate networks to lower search costs, and candidate networks are built based on a meta-architecture automatically adjusted regarding to the resource limits. In experiments, the search consumes 6.5 hours to find a network applicable to the edge device Jetson X2. The found network is then trained on camera trap images through a workstation and tested on Jetson X2. The network achieves competitive accuracies compared with the automatically and the manually designed networks.
Highlights
Ecosystems in earth have irreplaceable ecological, societal, and economic value for human beings [1], but ecosystems can be compositionally and functionally changed by species extinctions [2], e.g., massive declines in large carnivore populations are likely to result in ecosystem instability [3], loss of large herbivores can alter ecosystems through the loss of ecological interactions [4], and digging mammals are vital for maintaining the ecosystem in Australia [5]
Results and Discussion e performances of neural architecture search based on regression tree (NASRT) are reflected by both the search efficiency and the performance of the resulting convolutional neural networks (CNNs). e search is based on CIFAR-10 [40]. e CNN performance is evaluated on wildlife datasets ENA24 [41] and MCTI [42]
A neural architecture search method named NASRT is proposed for providing CNNs customized for diverse edge devices, and edge devices can be incorporated with clusters of camera traps to set up or expand surveillance areas. ere are mainly two challenges faced by NASRT, i.e., lowering search costs and searching networks feasible for edge devices
Summary
Ecosystems in earth have irreplaceable ecological, societal, and economic value for human beings [1], but ecosystems can be compositionally and functionally changed by species extinctions [2], e.g., massive declines in large carnivore populations are likely to result in ecosystem instability [3], loss of large herbivores can alter ecosystems through the loss of ecological interactions [4], and digging mammals are vital for maintaining the ecosystem in Australia [5]. Some wild animals like vertebrates high up the food chain may affect many other plants and animal species low down the chain [3, 4]. To prevent their extinctions, wildlife research, protection, and management require reliable animal data, such as population distributions, trying not to disturb animals and their habitats. Camera traps may produce millions of animal images requiring classification [8] which is commonly automated via machine learning or deep learning methods, especially through convolutional neural networks (CNNs) [9,10,11,12,13,14,15,16]. CNN-based methods are widely adopted in classifications, almost all methods are developed
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