An Ensemble of Deep Learning Models Using a Genetic Algorithm for Improving Bird Image Segmentation

B. S. Chandrashekar; H. S. Nagendraswamy; M. P. Pavan Kumar

doi:10.48084/etasr.17394

Authors

B. S. Chandrashekar Department of Studies in Computer Science, University of Mysore, Mysuru, Karnataka, India https://orcid.org/0009-0000-4655-4357
H. S. Nagendraswamy Department of Studies in Computer Science, University of Mysore, Mysuru, Karnataka, India https://orcid.org/0009-0001-2614-6169
M. P. Pavan Kumar Information Science and Engineering Department, Jawaharlal Nehru National College of Engineering, Shivamogga, Karnataka, India https://orcid.org/0000-0002-9372-9065

Volume: 16 | Issue: 3 | Pages: 36966-36978 | June 2026 | https://doi.org/10.48084/etasr.17394

Received: 7 January 2026 | Revised: 10 February 2026 and 24 February 2026 | Accepted: 6 March 2026 | Online: 6 June 2026

Corresponding author: B. S. Chandrashekar

Abstract

Bird image segmentation and classification from natural-scene images captured by a device are important for ecological research focused on wildlife conservation and monitoring. Manually segmenting a bird image from a complex background is tedious, time-consuming, and process-intensive. Therefore, an automatic segmentation of bird images from the complex scene image, exploiting image processing techniques, has been extensively studied. Although many efforts have been made to propose segmentation techniques in a different context, the techniques cannot be generalized and may not perform well in some cases. Besides, the task of segmentation is very challenging due to occlusion, morphology, and lighting conditions. Therefore, there is a scope to fine-tune and enhance the segmentation ability of algorithms. The present study proposes a novel method of ensembling deep learning models using Genetic Algorithm (GA) for improving bird image segmentation in a complex background. The proposed methodology employs the segmentation results obtained by the individual deep learning models at the pixel level for ensembling. Five popular deep learning models, namely Unet, PSP-net, Link-net, Feature Pyramid Network (FPN), and Deeplabv3+, are used in the study. The CUB-200-2011 benchmark dataset was employed to conduct experiments, and the efficacy of the proposed approach was evaluated using the Intersection over Union (IoU) metric. The segmentation results obtained were very promising, achieving the highest IoU compared to contemporary segmentation approaches.

Keywords:

segmentation, birds, genetic algorithm, chromosome, pixel-wise combination

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