Abstract:

[Objective] In the increasingly strict environment of data protection, the data privacy and security has hindered the application of deep learning technology in wildlife camera trap images. [Methods] In this paper, we use the open-source federated learning framework Flower and object detection model YOLOv8 to simulate the federated learning of wildlife camera trap images in real scenes on the public dataset for the first time. Multiple experiments are conducted for different local learning epochs of federated learning clients, and compared with traditional deep learning methods. [Results] In the experimental results, the mAP₅₀ of the global object detection models obtained by federated learning under different local epochs can reach 95% of that of traditional learning. To ensure data privacy and security, only a very small degradation in model performance is brought, indicating that federated learning has a very large application potential in the field of deep learning of wildlife camera trap images. Moreover, compared with the results of independent learning of each client of federated learning, each client is in an unfair position in federated learning, and the incentive mechanism for federated learning suitable for wildlife camera trap images needs to be further studied. [Conclusions] The multiple experiments of object detection with federated learning for wildlife camera trap images indicates that the application of federated learning on camera trap images has great significance for measuring and monitoring global biodiversity changes.

Key words: wildlife, camera traps, object detection, federated learning, deep learning