We present simple and flexible drop-in modules in feature pyramids for general object detection, which can be easily generalized to other anchor-free detectors without introducing extra parameters, and only involves negligible computational cost on training and testing. The proposed detector, called NCMS, inserts a simple norm calibration (NC) operation between the feature pyramids and detection head to alleviate and balance the norm bias caused by feature pyramid network (FPN). Furthermore, the NCMS leverages an enhanced multi-feature selective strategy (MS) during training to assign the ground-truth to particular feature pyramid levels as supervisions, in order to obtain more discriminative representation for objects. By generalizing to the state-of-the-art FSAF module (Zhu et al., 2019), our NCMS improves it by 1.6% on COCO val set without bells and whistles. The resulting best model achieves 44.0% mAP with single-model and single-scale testing, which is a fairly competitive result.

我们在特征金字塔中提供了简单灵活的插入式模块，用于一般物体的检测，可以轻松地将其推广到其他无锚检测器，而无需引入额外的参数，并且在训练和测试方面的计算成本可忽略不计。提议的检测器称为NCMS，在特征金字塔和检测头之间插入一个简单的规范校准（NC）操作，以减轻和平衡由特征金字塔网络（FPN）引起的规范偏差。此外，NCMS在训练期间利用增强的多特征选择策略（MS）将地面真相分配给特定的特征金字塔级别作为监督，以便获得对象的更多判别表示。通过归纳为最新的FSAF模块（Zhu等人，2019），我们的NCMS将其改进了1。不带铃铛的COCO瓦尔套装的6％。最终的最佳模型通过单模型和单规模测试达到了44.0％的mAP，这是一个相当有竞争力的结果。