关键词: 目标检测, 寄生虫卵, 部分卷积, 特征融合, 注意力模块

Abstract: In order to address the detection difficulty of small parasite eggs, occluded targets and low resolution in microscopic images, a lightweight object detection model named LCN-YOLOv8(Lightweight Convolution Network YOLOv8) was proposed in this paper. Firstly, a new feature extraction module PA-C2f(Patial Attention C2f) was designed to replace all C2f modules. It incorporated partial convolution with a separated and enhancement attention module, which could reduce the number of parameters and maintain efficient feature extraction ability. Then, Space-to-depth Convolution was introduced to replace the standard convolution, reducing model complexity and feature loss of low-resolution parasite eggs. Finally, a Lightweight High-level Screening-feature Feature Pyramid Network was designed to enhance the model's ability of multi-scale feature fusion and detection performance for occluded parasite eggs. Compared with the baseline network YOLOv8s, LCN-YOLOv8 improves mAP (mean Average Precision)@0.5:0.95 on single and multi-eggs datasets by 1.7 and 1.6 percentage points, respectively, reduces parameters and computation by 6.0×10^6 and 7.5 GFLOPs. Experimental results demonstrate that LCN-YOLOv8 achieves an improved balance between detection accuracy and model complexity, which can assist in the diagnosis of parasitic diseases.

Key words: object detection, parasite eggs, partial convolution, feature fusion, attention module