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Scientia Silvae Sinicae ›› 2025, Vol. 61 ›› Issue (4): 1-8.doi: 10.11707/j.1001-7488.LYKX20240437

• Special subject: Smart forestry • Previous Articles    

A Recognition Method of Domain Adaptation for Wildlife Images Based on Adversarial Learning

Zhao Enting1, Zhang Changchun1, Zhao Haitao2, Zhang Junguo1   

  1. 1. School of Technology, Beijing Forestry University　State Key Laboratory of Efficient Production of Forest Resources　Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation　Beijing 100083;
    2. Shaanxi Institute of Zoology　Xi’an 710032
  • Received:2024-07-15 Revised:2025-02-12 Published:2025-04-21

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Abstract: Objective To address the domain shift issues caused by different spatiotemporal scenarios and species variations, this study proposes a domain adaptation recognition method for wildlife images based on adversarial learning. The aim of this study is to enhance the generalization performance of unlabeled wildlife species recognition in complex field environment, thereby providing a robust theoretical foundation for the classification of wildlife in open environment. Method An adversarial learning network model was constructed by utilizing wildlife image category information as conditional inputs. Batch spectral penalty constraints and mixup feature alignment methods were employed to mitigate distribution discrepancies among wildlife images across different spatiotemporal scenarios and species variations. This framework established a feature alignment model based on adversarial learning to enhance image recognition performance.Result The proposed method was trained and evaluated on domain adaptation datasets containing 8 and 11 wildlife species, respectively. Compared to baseline methods combining adversarial learning and feature alignment, the average recognition accuracy of the proposed method increased by 3.3% and 14.0%, the precision was improved by 3.3% and 20.6%, the recall rate was improved by 3.5% and 20.5%, and the F1-score was improved by 3.6% and 5.1%, respectively. The wild animal image domain adaptation model based on adversarial learning significantly improved performance for wildlife recognition in cross-domain scenarios. Conclusion The wildlife category information used as conditional inputs in the adversarial learning network can provide multimodal structural information of wildlife images, which enables the proposed method in this study to better understand the inter-image relationships and improve domain adaptation learning capabilities. The better the alignment of image features between the training and testing sets, the better the image recognition performance of the testing set. This study provides novel insights and methods for cross-domain wildlife image recognition research.

Key words: wildlife, recognition method, adversarial learning, feature alignment, domain adaptation

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