<![CDATA[Automated monitoring is essential for sea turtle conservation, and vision-based algorithms offer a non-intrusive approach to observing turtles without disturb ing their natural habitat. However, nighttime conditions introduce significant challenges, as low illumination severely reduces the visibility of discriminative features. This limitation requires robust feature extraction to capture the lim ited important features. Recent advances in computer vision, such as you only look once (YOLO) 11-nano, enable real-time detection and allow optimization under challenging conditions. In this study, we propose NiteTron, a nighttime sea turtle species detector that uses a lightweight architecture with enhanced feature refinement. The network modifies YOLO11-nano to create a more ef ficient variant, YOLO11-pico. To improve detection in low-light scenarios, we introduce a strong feature modulation (SFM) module. This module enhances attention to relevant features by expanding the receptive field in the final back bone stage. We also incorporate a sparse separated convolution (S2C) module to extract key information efficiently while balancing accuracy and computational cost. The proposed model achieves high precision with fewer parameters and lower giga floating-point operations per second (GFLOPs) than YOLO11-nano. Experiments demonstrate superior performance, achieving 0.682 mean average precision (mAP)@0.5:0.95 for nighttime sea turtle detection. Inference tests also confirm efficient central processing unit (CPU) deployment, reaching 21.18 frames per second (FPS) on a desktop and 2.65 FPS on an edge device.]]>
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