Lightweight Enhancement Module for Efficient Sea Turtle Species Detection under Illumination Variation

Authors

Yuliana Mose
Trinita University
Alex Copernikus Andaria
Trinita University
Hebron Prasetya
Sam Ratulangi University image/svg+xml
Muhamad Dwisnanto Putro
Sam Ratulangi University image/svg+xml
DOI: https://doi.org/10.52549/ijeei.v14i2.7399
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Published: 22-04-2026 • Issue: Vol. 14 No. 2: June 2026 • Section: Regular paper

Abstract

Sea turtle monitoring is crucial for maintaining ecosystem balance, although traditional methods are costly and inefficient. Object detection provides a foundation for autonomous monitoring by localizing sea turtles in their natural habitat. At the same time, recent deep learning models can operate in real-time. On the other hand, underwater detection remains challenging due to extreme illumination, underwater blur, and underwater distortions, which obscure key features from the background. To address these issues, this work proposes a new sea turtle detector, based on a modified YOLOv12-nano model that incorporates a lightweight enhancement module to improve robustness under diverse illumination. The network introduced Efficient Spatial Convolution (ESC) at the end of the backbone to improve the representation of high-level features. It involves Dual Spatial Convolution (DSC) to improve backbone feature extraction with a large kernel size while preserving efficiency. The Lite module is designed to enrich feature diversity and selectively enhance the quality of high-level features. These modifications significantly enhance detection performance under diverse underwater lighting conditions. The proposed network achieves higher precision than the original YOLOv12-nano while maintaining efficiency, making it well-suited for deployment on low-cost devices.
Keywords: Sea turtle; Species detection; Illumination; Lightweight Enhancement; Efficient deep learning

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How to Cite

[1]
Y. Mose, A. C. Andaria, H. Prasetya, and M. D. Putro, “Lightweight Enhancement Module for Efficient Sea Turtle Species Detection under Illumination Variation”, IJEEI, vol. 14, no. 2, pp. 475–492, Apr. 2026, doi: 10.52549/ijeei.v14i2.7399.