YOLO11-DBalgae: An Enhanced Deep Learning Framework for Robust Microalgal Detection

Accurate and rapid identification of microalgae in ship ballast water is critical for preventing the spread of invasive aquatic species and ensuring ecological security. However, traditional manual microscopic examination is labor-intensive and limited by challenges such as high intra-class morphological variability, frequent cell aggregation, and inter-class similarity among microalgae. This study proposes YOLO11-DBalgae, a specialized end-to-end object detection framework designed for fine-grained microalgae recognition in complex aquatic environments. Two key architectural innovations are introduced into the YOLO11n baseline—a Detail-enhanced Vanishing-prevention Block (DVB), which processes input features through a VoVGSCSP cross-stage aggregation module followed by parallel Conv and DSConv paths, preserving fine-grained boundary signals of morphologically diverse algal cells during repeated downsampling, and a Bidirectional Feature Pyramid Network (BiFPN), which employs learnable cross-scale weighting to optimize multi-scale feature fusion across the extreme size range of co-occurring microalgal targets. Experimental results demonstrate that YOLO11-DBalgae achieves an mAP@0.5 of 97.3%, representing an improvement of 7.0 percentage points over the baseline YOLO11n model. The model sustains an inference speed of 240 FPS with 2.83 M parameters, maintaining a lightweight and deployment-viable profile. Qualitative analysis via per-class precision–recall curves, detection visualization, and Grad-CAM attention maps confirms the model’s robustness in recovering near-invisible weak-feature targets, minimizing false detections within dense cell clusters, and accurately distinguishing morphologically convergent species. The proposed framework provides a practical and deployable solution for automated microalgae monitoring, offering maritime regulatory bodies an efficient and reliable tool for ballast water management.