Multi-Scale Attention-Augmented YOLOv8 for Real-Time Surface Defect Detection in Fresh Soybeans

Ensuring the surface quality of fresh soybeans is critical for maintaining their commercial value and consumer confidence. However, traditional manual inspection remains labor-intensive, subjective, and inadequate for real-time, high-throughput sorting. In this study, we present a multi-scale attention-augmented You Only Look Once version 8 (YOLOv8) framework tailored for real-time surface defect detection in fresh soybeans. The proposed model integrates two complementary attention mechanisms—Squeeze-and-Excitation (SE) and Multi-Scale Dilated Attention (MSDA)—to enhance the detection of small, irregular, and low-contrast defects under complex backgrounds. Rather than relying on cross-model comparisons, we perform systematic ablation studies to evaluate the individual and combined contributions of SE and MSDA across diverse defect categories. Experimental results from a custom-labeled soybean dataset demonstrate that the integrated SE+MSDA model achieves superior performance in terms of precision, recall, and Mean Average Precision (mAP), particularly for challenging categories such as wormholes and speckles. The proposed framework provides a lightweight, interpretable, and deployment-ready solution for intelligent agricultural inspection, with potential applicability to broader food quality control tasks.