Abstract
Vaccines play a crucial role in the prevention and control of chicken coccidiosis, effectively reducing economic losses in the poultry industry and significantly improving animal welfare. To ensure the production quality and immune effect of vaccines, accurate detection of chicken Coccidia oocysts in vaccine is essential. However, this task remains challenging due to the minute size of oocysts, variable spatial orientation, and morphological similarity among species. Therefore, we propose YOLO-Cocci, a chicken coccidia detection model based on YOLOv8n, designed to improve the detection accuracy of chicken coccidia oocysts in vaccine environments. Firstly, an efficient multi-scale attention (EMA) module was added to the backbone to enhance feature extraction and enable more precise focus on oocyst regions. Secondly, we developed the inception-style multi-scale fusion pyramid network (IMFPN) as an efficient neck. By integrating richer low-level features and applying convolutional kernels of varying sizes, IMFPN effectively preserves the features of small objects and enhances feature representation, thereby improving detection accuracy. Finally, we designed a lightweight feature-reconstructed and partially decoupled detection head (LFPD-Head), which enhances detection accuracy while reducing both model parameters and computational cost. The experimental results show that YOLO-Cocci achieves an mAP@0.5 of 89.6%, an increase of 6.5% over the baseline model, while reducing the number of parameters and computation by 14% and 12%, respectively. Notably, in the detection of Eimeria necatrix, mAP@0.5 increased by 14%. In order to verify the application effect of the improved detection algorithm, we developed client software that can realize automatic detection and visualize the detection results. This study will help improve the level of automated assessment of vaccine quality and thus promote the improvement of animal welfare.