Open Access

ARTICLE

Recognition of Bird Species of Yunnan Based on Improved ResNet18

Wei Yang^1,2,*, Ivy Kim D. Machica¹

1 College of Information and Computing, University of Southeastern Philippines, Davao City, 8000, Philippines
2 College of Big Data, Baoshan University, Baoshan, 678000, China

* Corresponding Author: Wei Yang. Email:

Intelligent Automation & Soft Computing 2024, 39(5), 889-905. https://doi.org/10.32604/iasc.2024.055133

Received 18 June 2024; Accepted 05 August 2024; Issue published 31 October 2024

Abstract

Birds play a crucial role in maintaining ecological balance, making bird recognition technology a hot research topic. Traditional recognition methods have not achieved high accuracy in bird identification. This paper proposes an improved ResNet18 model to enhance the recognition rate of local bird species in Yunnan. First, a dataset containing five species of local birds in Yunnan was established: C. amherstiae, T. caboti, Syrmaticus humiae, Polyplectron bicalcaratum, and Pucrasia macrolopha. The improved ResNet18 model was then used to identify these species. This method replaces traditional convolution with depth wise separable convolution and introduces an SE (Squeeze and Excitation) module to improve the model’s efficiency and accuracy. Compared to the traditional ResNet18 model, this improved model excels in implementing a wild bird classification solution, significantly reducing computational overhead and accelerating model training using low-power, lightweight hardware. Experimental analysis shows that the improved ResNet18 model achieved an accuracy of 98.57%, compared to 98.26% for the traditional Residual Network 18 layers (ResNet18) model.

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