An automatic method for removing empty camera trap images using ensemble learning

Abstract

Camera traps often produce massive images, and empty images that do not contain animals are usually overwhelming. Deep learning is a machine‐learning algorithm and widely used to identify empty camera trap images automatically. Existing methods with high accuracy are based on millions of training samples (images) and require a lot of time and personnel costs to label the training samples manually. Reducing the number of training samples can save the cost of manually labeling images. However, the deep learning models based on a small dataset produce a large omission error of animal images that many animal images tend to be identified as empty images, which may lead to loss of the opportunities of discovering and observing species. Therefore, it is still a challenge to build the DCNN model with small errors on a small dataset. Using deep convolutional neural networks and a small‐size dataset, we proposed an ensemble learning approach based on conservative strategies to identify and remove empty images automatically. Furthermore, we proposed three automatic identifying schemes of empty images for users who accept different omission errors of animal images. Our experimental results showed that these three schemes automatically identified and removed 50.78%, 58.48%, and 77.51% of the empty images in the dataset when the omission errors were 0.70%, 1.13%, and 2.54%, respectively. The analysis showed that using our scheme to automatically identify empty images did not omit species information. It only slightly changed the frequency of species occurrence. When only a small dataset was available, our approach provided an alternative to users to automatically identify and remove empty images, which can significantly reduce the time and personnel costs required to manually remove empty images. The cost savings were comparable to the percentage of empty images removed by models.

Automated Detection of Koalas with Deep Learning Ensembles

“…The best performing ensemble contained the greatest diversity of component models, demonstrating the benefit of ensemble learning which exploits the various architectural strengths and minimises the weaknesses [29][30][31][32][33]. In the validation phase, YOLO models consistently featured in high-performing ensembles, suggesting that their inclusion may be valuable when constructing ensembles for processing low-altitude imagery.…”

“…A study published in 2019 notably achieved high accuracy for koala detection in complex arboreal habitats by fusing the output of two common CNNs (YOLO and Faster R-CNN) over multiple frames [18], which can be considered a primitive form of ensemble learning. Ensemble learning is well established in the computer vision community and involves the integration of multiple deep-learning algorithms into single, larger detector models, exploiting the inherent differences in the capabilities of different model architectures while minimising the weaknesses [29][30][31][32][33]. Ensemble learning improves model performance [29], increases predictive inference [31,34] and reduces model-based uncertainty compared to a single model approach [32,35].…”

“…Ensembles have recently been applied to image analysis tasks, such as classifying cheetah behaviours [36] and multilevel image classes [30], and for the re-identification of tigers [37]. But apart from [33], who used ensemble learning to identify empty images in camera trap data, there has been little exploration of the enhanced predictive and computational power of ensembles for detection of wildlife from remote sensing data. This represents a considerable opportunity to the ecological community.…”

The Role of Citizen Science and Deep Learning in Camera Trapping

“…Full compatibility with the other main standards is the subject of future challenges, especially with the integration of larger Citizen Science infrastructures, such as SciStarter or Zooniverse. An automatic detection of empty records [42] or people in the images in accordance with GDPR law, together with an identification of the content, starts immediately as soon as the new records are successfully uploaded. Empty records are labelled and filtered out from the data in the subsequent workflow; people detected in the images are irreversibly masked, and the images are labelled.…”