Wildlife Images Recognition Method Based on Wasserstein Distance and Correlation Alignment Transfer Learning

【Objective】This study aims to address the influence of complex factors such as lighting, background, and shooting scale on the accuracy of wildlife image recognition. 【 Method】 In this study, the wild animal images captured by infrared triggered cameras in the wild were used as the object: 1) Two publicly available wildlife datasets, ENA24 and NACTI, were used to construct disjoint datasets S1 and S2, comprising a total of 11 animal categories and 25 591 images. 2) To tackle domain shift issues, a ResNet50 network was utilized as a feature extraction module to build a domain adversarial network, effectively alleviating domain bias. 3) A representation learning network incorporating Wasserstein distance and correlation alignment was proposed to establish a transfer learning network for feature extraction and recognition, so as to further exploit transferable features. 【Result】The performance of different models in wildlife recognition was evaluated using the average accuracy metric. Results indicated that the average accuracy on 11 wildlife categories for eight models, namely ResNet50, DDC, DCORAL, DAN, DANN, CDAN, HAN, and JTN, was 48.4%, 51.6%, 49.6%, 52.6%, 45.2%, 50.9%, 54.6%, and 53.5%, respectively. Upon enhancing the ResNet50 base model with improved residual modules and introducing a representation learning network incorporating Wasserstein distance and correlation alignment, the average accuracy for 11 wildlife categories was improved by 2.7% compared to the existing best result with the comparative methods.【 Conclusion】 The transfer learning method based on Wasserstein distance and correlation alignment has achieved an average accuracy of 57.3% in wildlife recognition. The introduction of representation learning based on Wasserstein distance and correlation alignment can effectively improve the accuracy of the wildlife recognition model. © 2024 Chinese Society of Forestry. All rights reserved.