YOLOv8-SLG: A Small-Object Detection Model for Gobiocypris rarus Larvae in Laboratory Settings

Accurate detection and counting of Gobiocypris rarus larvae are essential for laboratory toxicological experiments and aquatic biological research. However, the larvae detection task remains challenging due to small object size, dense aggregation, rapid movement, and complex imaging backgrounds. To address these challenges, this study proposes an improved detection framework termed YOLOv8-SLG, an optimized small-object detection framework built upon YOLOv8n and specifically designed for high-density fish larvae scenarios. The proposed model incorporates three task-oriented structural enhancements. First, a lightweight C3Ghost module is introduced into the Neck to reduce parameter redundancy while maintaining multi-scale feature fusion capability. Second, an enhanced SPPF-LSKA module integrates large-kernel spatial attention into multi-scale feature aggregation, strengthening contextual modeling and boundary discrimination. Third, an SPD-Conv module preserves fine-grained spatial information during downsampling, mitigating detail loss that typically degrades small-object localization performance. Experimental results on a self-constructed laboratory dataset demonstrate consistent improvements over the baseline YOLOv8n. YOLOv8-SLG increases Precision by 2.6 percentage points and Recall by 1.9 percentage points, [email protected] by 0.7%, and notably [email protected]–0.95 by 3.4%. The larger gain under stricter IoU thresholds indicates improved localization performance under the evaluated conditions. Despite a moderate increase in computational complexity (from 8.1 to 11.6 GFLOPs), YOLOv8-SLG maintains a compact model size (6.6 MB), indicating an accuracy–computation trade-off for improved detection performance under controlled laboratory conditions. Overall, YOLOv8-SLG provides improved detection accuracy, with an acceptable increase in computational cost under controlled laboratory conditions, and interpretability, providing a promising and reproducible framework for automated larvae detection and counting under controlled laboratory conditions.