Lightweight Enhancement Module for Efficient Sea Turtle Species Detection under Illumination Variation - Yuliana Mose; Alex Copernikus Andaria; Hebron Prasetya; Muhamad Dwisnanto Putro (Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 2026)
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.