Backbone and Feature Fusion Design for YOLOv8-Based Bacterial Microcolony Detection in Microscopy Images

Foodborne bacterial contamination creates significant public health and economic challenges. In the United States, the CDC estimates that foodborne illness causes approximately 48 million illnesses and 3000 deaths annually. Rapid screening is important because conventional confirmation methods are time- and labor-intensive. Microscopy-based analysis of early bacterial microcolonies can enable detection within hours rather than days, yet manual inspection is slow, subjective, and impractical at scale. Although deep learning object detectors such as YOLO offer a promising solution, the impact of architectural design choices on microscopy-based bacterial detection has not been systematically characterized under controlled conditions. In this work, we conducted a controlled architectural evaluation of YOLOv8 for detecting bacterial microcolonies in high-resolution microscopy images. We replaced the CSP-Darknet backbone with EfficientNetV2 variants and evaluated three feature fusion designs: no neck, the original PAN-FPN neck, and a NAS-FPN-inspired neck. All experiments were performed under identical conditions on a two-class dataset of Salmonella and E. coli. Our results show that EfficientNetV2 architectures consistently outperform the YOLOv8x baseline, which achieved 0.891 precision, 0.867 recall, and 0.898 mAP@50. The best overall performance was obtained with EfficientNetV2-S and the original YOLOv8 neck, reaching 0.976 precision, 0.968 recall, and 0.987 mAP@50, with comparable performance of 0.986 mAP@50 achieved by EfficientNetV2-S + NAS-FPN. The highest precision was obtained with EfficientNetV2-L + NAS-FPN, reaching 0.978. These findings demonstrate that effective bacterial detection depends on the interaction between backbone capacity and feature fusion design rather than backbone scaling alone.