An Improved Microbial Object Detection Method for Low-Contrast and Occluded Scenarios Based on SMA-YOLOv8s

Accurate detection and localization of microbial targets are critical for microbial trajectory tracking and analysis. However, microscopic microorganism images often exhibit low contrast and mutual occlusion between targets, which pose significant challenges for microbial object accuracy detection due to insufficient distinguishable shallow-layer information and occluded targets inadequate representation. To address these issues, a novel method of SMA-YOLOv8s is proposed for microbial object detection. Firstly, the traditional strided convolutions is replaced with SPD-Conv in downsampling of YOLOv8s to retain shallow-layer information. Secondly, a feature fusion strategy that integrates Cascaded Group Attention with Scale Sequence Feature Fusion (CSFF) is proposed, which could enrich contextual feature representation for better detecting occluded targets. Thirdly, the Wise-IoU loss function is employed to optimize bounding box regression, improving localization precision. Experimental evaluations on the BCCD, CTMCv1, and a self-constructed microscopic microorganism dataset demonstrate that SMA-YOLOv8s achieves mAP50 scores of 95.5%, 90.3%, and 81.7%, respectively, surpassing baseline methods in overall performance. These results highlight the robustness and effectiveness of the proposed method in detecting microbial targets under low contrast and occlusion conditions.