Intersection-over-Union (IoU), as a pivotal metric for evaluating the spatial alignment between candidate proposals and ground-truth annotations, directly determines the quality of positive sample sets and the training efficacy of visual detection models. Through theoretical modeling and analysis, we uncover a non-sensitive region on the IoU response curve, within which samples yield nearly identical IoU scores despite distinct geometric overlaps. To overcome this limitation, we introduce a set of morphological similarity metrics covering area, shape, and aspect ratio, to refine the positive sample assignment process, thereby ensuring more discriminative and reliable matching. A supplementary matching score is derived via mean-based aggregation of these multidimensional similarities, compensating for the intrinsic limitation of IoU in representing structural correspondence. Theoretically, incorporating morphological similarity reshapes the response distribution of the matching function, yielding both effective directional gradients and polygon-like iso-response contours, which tightly confine high-response regions around each ground-truth instance and substantially enhance the precision of positive sample selection. Experiments based on the YOLOv9 framework demonstrate consistent performance gains on both NEUDET and GC10- DET datasets. Notably, the proposed approach is fully plug-and-play and incurs zero additional inference overhead, thereby ensuring deployment efficiency for industrial visual inspection.

翻译：交并比（Intersection-over-Union，IoU）作为评估候选提议与真实标注之间空间对齐的关键指标，直接决定了正样本集的质量以及视觉检测模型的训练效能。通过理论建模与分析，我们揭示了IoU响应曲线中存在一个不敏感区域，在此区域内，尽管样本具有不同的几何重叠程度，但其IoU得分却近乎相同。为克服这一局限，我们引入涵盖面积、形状及长宽比的一组形态相似度度量，对正样本分配过程进行精细化改进，从而确保更为判别性且可靠的匹配。通过基于这些多维相似度的均值聚合，推导出一个补充匹配得分，以此补偿IoU在表征结构对应性方面的固有不足。理论上，融入形态相似度重塑了匹配函数的响应分布，产生有效的方向性梯度及多边形等值响应轮廓，这些轮廓将高响应区域紧密约束于每个真实标注实例周围，显著提升了正样本选择的精度。基于YOLOv9框架的实验表明，在NEUDET和GC10-DET数据集上均获得一致的性能提升。值得注意的是，所提方法具有完全即插即用的特性，且不增加任何额外推理开销，从而确保了工业视觉检测中的部署效率。