In the development of intelligent processing for fresh corn cobs, automated inspection of ear appearance quality to promptly sort out cobs with surface defects and ensure overall product compliance is currently a hot topic in agricultural product processing research. However, fresh corn cob surfaces are covered with numerous independent, densely packed kernels, and defects affecting one or more kernels create surface anomalies of highly variable sizes. This leads to defect targets with multi-scale features and scattered distributions, making it challenging for existing deep learning-based visual inspection methods to simultaneously optimize small-target modeling capacity and computational efficiency. Consequently, these methods cannot effectively balance the accuracy of small-scale defect detection with computational efficiency, making it difficult to meet practical requirements. To address these issues, this paper proposes the SSAD-YOLOv8s-Prune defect detection method for small-scale defect detection in white fresh corn cobs. First, the backbone layer of the original model is replaced with a custom-designed SSA structure, which not only expands the feature dimensions for small-scale defects and enriches feature representation but also reduces the number of computational parameters to achieve model compression. Second, the original neck layer is replaced with a custom-designed RepDyFPN structure to enable feature fusion and interaction across different scales and depths. Finally, the LAMP algorithm is employed to prune and compress the newly improved model, further achieving model compression performance. Compared with the baseline YOLOv8s, our method reduces model parameters by 9.33 M, floating-point operations (FLOPs) by 12.5 G, and model size by 17.6 MB, while simultaneously improving mAP50 by 1.2 percentage points to 96.1% and mAP50–95 by 4.1 percentage points to 62.8%. Furthermore, our method maintains advantages over other mainstream detection models. Therefore, the proposed SSAD-YOLOv8-Prune detection model successfully balances detection accuracy with model compression, providing a feasible detection method for small-scale defect detection in fresh corn cobs.
Zhang et al. (Fri,) studied this question.