A Robust RMSFED–ASPP Enhanced YOLOv8 Framework for Detecting Poultry Abnormalities

Automated detection of poultry leg and eye abnormalities is essential for efficient farm management and animal welfare. This paper presents a deep learning framework that integrates Residual Multi-Scale Feature Enhancement Decoder (RMSFED) with Atrous Spatial Pyramid Pooling (ASPP) and YOLOv8. The proposed architecture preserves fine-grained features, enhances multi-scale contextual understanding, and accurately identifies small lesions under challenging farm conditions such as varying illumination, occlusions, and cluttered backgrounds. YOLOv8 efficiently extracts Regions of Interest (ROI) for downstream analysis. Experiments on real poultry datasets demonstrate that the RMSFED+ASPP integrated model outperforms baseline YOLOv8 and YOLOv8+ASPP variants in Dice Coefficient, Sensitivity, Specificity, IoU, and F1-Score. Visual assessments confirm improved boundary localization and reduced false detections. The framework supports real-time inference, offering a robust and reliable tool for automated poultry health monitoring.