论文标题
一个三层插件，以改善闭塞检测
A Tri-Layer Plugin to Improve Occluded Detection
论文作者
论文摘要
检测被遮挡的对象仍然是最新对象探测器的挑战。这项工作的目的是改善此类对象的检测，从而改善现代对象探测器的整体性能。 为此，我们做出以下四个贡献：（1）我们为两个阶段对象检测器的检测头提出了一个简单的“插件”模块，以改善部分遮挡的对象的回忆。该模块可以预测目标对象，封闭器和封闭器的分割掩码的三层层，并且通过这样做可以更好地预测目标对象的掩码。 （2）我们通过使用AMODAL完成现有对象检测和实例细分培训数据集来建立遮挡关系，为模块生成训练数据，为模块生成训练数据。 （3）我们还建立了一个可可评估数据集，以测量部分阻塞和分离对象的回忆性能。 （4）我们表明，仅通过微调检测头，插入两个阶段检测器的插件可以显着提高性能，如果整个架构进行了微调，则可以进行其他改进。据报道，用Swin-T或Swin-S主链的面膜R-CNN据报道可可结果，以及带有SWIN-B主链的级联面罩R-CNN。
Detecting occluded objects still remains a challenge for state-of-the-art object detectors. The objective of this work is to improve the detection for such objects, and thereby improve the overall performance of a modern object detector. To this end we make the following four contributions: (1) We propose a simple 'plugin' module for the detection head of two-stage object detectors to improve the recall of partially occluded objects. The module predicts a tri-layer of segmentation masks for the target object, the occluder and the occludee, and by doing so is able to better predict the mask of the target object. (2) We propose a scalable pipeline for generating training data for the module by using amodal completion of existing object detection and instance segmentation training datasets to establish occlusion relationships. (3) We also establish a COCO evaluation dataset to measure the recall performance of partially occluded and separated objects. (4) We show that the plugin module inserted into a two-stage detector can boost the performance significantly, by only fine-tuning the detection head, and with additional improvements if the entire architecture is fine-tuned. COCO results are reported for Mask R-CNN with Swin-T or Swin-S backbones, and Cascade Mask R-CNN with a Swin-B backbone.
