This paper presents HoughNet, a one-stage, anchor-free, voting-based,
bottom-up object detection method. Inspired by the Generalized Hough Transform,
HoughNet determines the presence of an object at a certain location by the sum
of the votes cast on that location. Votes are collected from both near and
long-distance locations based on a log-polar vote field. Thanks to this voting
mechanism, HoughNet is able to integrate both near and long-range,
class-conditional evidence for visual recognition, thereby generalizing and
enhancing current object detection methodology, which typically relies on only
local evidence. On the COCO dataset, HoughNet's best model achieves 46.4 $AP$
(and 65.1 $AP_{50}$), performing on par with the state-of-the-art in bottom-up
object detection and outperforming most major one-stage and two-stage methods.
We further validate the effectiveness of our proposal in another task, namely,
"labels to photo" image generation by integrating the voting module of HoughNet
to two different GAN models and showing that the accuracy is significantly
improved in both cases. Code is available at
this https URL

本文介绍一种基于投票的、一阶段的、无锚点的、自底向上的目标检测方法 HoughNet，它通过广义 Hough 变换在某个位置上的票数之和来确定目标的存在，并根据对数极坐标投票字段从近处和远处位置收集票数，从而集成近距离和远距离的分类条件证据以进行视觉识别，从而推广和增强当前的目标检测方法。在 COCO 数据集上，HoughNet 的最佳模型达到了 46.4 $AP$（和 65.1 $AP_{50}$），在自底向上的目标检测中表现与最先进的方法相当，并且在大多数主要的一阶段和两阶段方法中表现更好。我们进一步通过将 HoughNet 的投票模块集成到两个不同的 GAN 模型中并显示在两种情况下准确度显著提高来验证我们方法的有效性。