Towards an IoT-based Deep Learning Architecture for Camera Trap Image Classification

Maintaining biodiversity is a key component of the United Nations (UN) “Life on Land” sustainability goal. Remote camera traps monitoring animals' movements support research in biodiversity. However, images from these camera traps are currently labeled manually resulting in high processing costs and long delays. This paper proposes an IoT -based system that leverages deep learning and edge computing to automatically label camera trap images and transmit this information to scientists in a timely manner. Inception-V3, MobileNet-V2, ResNet-18, and DenseNet-121 were trained on data consisting of 33,984 images taken during day and night with 6 animal classes. Inception- V3 yielded the highest macro average F1-score of 0.93 and an accuracy of 94%. An IoT-based system was developed that directly captures images from a commercial camera trap, does the inference on the edge using a Raspberry Pi (RPi), and sends the classification results back to a cloud database system. A mobile App is used to monitor the camera images classified on camera traps in real-time. The RPi could easily sustain a rate of processing 1 image every 2 seconds with an average latency of 1.8 second/image. After capture and pre-processing, each inference took an average of 0.2 Millisecond/image on a RPi Model 4B.