A Lightweight Automatic Wildlife Recognition Model Design Method Mitigating Shortcut Learning

SIMPLE SUMMARY: Due to the complexity of the wild environment, wildlife recognition based on camera trap images is challenging. Indeed, as the backgrounds of images captured from the same infrared camera trap are rather similar, shortcut learning of recognition models are produced, resulting in reduced generality and poor recognition model performance. Therefore, we propose a data augmentation strategy that integrates image synthesis (IS) and regional background suppression (RBS). This strategy alleviates a model’s focus on the background, guiding it to focus on the wildlife in order to improve the model’s generality, resulting in better recognition performance. Furthermore, in order to offer the lightweight recognition model for deep learning-based real-time wildlife monitoring on edge devices, we developed a model compression strategy that combines adaptive pruning and knowledge distillation. The produced lightweight model can reduce the computational effort of wildlife recognition with less loss of accuracy and is beneficial for real-time wildlife monitoring with the use of edge intelligence. ABSTRACT: Recognizing wildlife based on camera trap images is challenging due to the complexity of the wild environment. Deep learning is an optional approach to solve this problem. However, the backgrounds of images captured from the same infrared camera trap are rather similar, and shortcut learning of recognition models occurs, resulting in reduced generality and poor recognition model performance. Therefore, this paper proposes a data augmentation strategy that integrates image synthesis (IS) and regional background suppression (RBS) to enrich the background scene and suppress the existing background information. This strategy alleviates the model’s focus on the background, guiding it to focus on the wildlife in order to improve the model’s generality, resulting in better recognition performance. Furthermore, to offer a lightweight recognition model for deep learning-based real-time wildlife monitoring on edge devices, we develop a model compression strategy that combines adaptive pruning and knowledge distillation. Specifically, a student model is built using a genetic algorithm-based pruning technique and adaptive batch normalization (GA-ABN). A mean square error (MSE) loss-based knowledge distillation method is then used to fine-tune the student model so as to generate a lightweight recognition model. The produced lightweight model can reduce the computational effort of wildlife recognition with only a 4.73% loss in accuracy. Extensive experiments have demonstrated the advantages of our method, which is beneficial for real-time wildlife monitoring with edge intelligence.