Pedestrian detection based on visual sensors has made significant progress, in which region proposal is the key step. There are two mainstream methods to generate region proposals: anchor-based and anchor-free. However, anchor-based methods need more hyperparameters related to anchors for training compared with anchor-free methods. In this paper, we propose a novel multiscale anchor-free (MSAF) region proposal network to obtain proposals, especially for small-scale pedestrians. It usually has several branches to predict proposals and assigns ground truth according to the height of pedestrian. Each branch consists of two components: one is feature extraction, and the other is detection head. Adapted channel feature fusion (ACFF) is proposed to select features at different levels of the backbone to effectively extract features. The detection head is used to predict the pedestrian center location, center offsets, and height to get bounding boxes. With our classifier, the detection performance can be further improved, especially for small-scale pedestrians. The experiments on the Caltech and CityPersons demonstrate that the MSAF can significantly boost the pedestrian detection performance and the log-average miss rate (MR) on the reasonable setting is 3.97% and 9.5%, respectively. If proposals are reclassified with our classifier, MR is 3.38% and 8.4%. The detection performance can be further improved, especially for small-scale pedestrians.

中文翻译：

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行人检测的多尺度无锚区域提议网络

基于视觉传感器的行人检测取得了重大进展，其中区域提议是关键步骤。生成区域建议的主流方法有两种：基于锚点的和无锚点的。但是，与无锚方法相比，基于锚的方法需要更多与锚相关的超参数进行训练。在本文中，我们提出了一个新颖的多尺度无锚（MSAF）区域投标网络，以获取投标，尤其是针对小规模行人的投标。它通常有几个分支机构来预测建议并根据行人的身高分配地面真相。每个分支都包含两个组件：一个是特征提取，另一个是检测头。提出了自适应信道特征融合（ACFF）来选择骨干不同级别的特征以有效地提取特征。检测头用于预测行人中心的位置，中心偏移和高度，以获取边界框。使用我们的分类器，可以进一步提高检测性能，特别是对于小规模的行人。在Caltech和CityPersons上进行的实验表明，在合理的设置下，MSAF可以显着提高行人检测性能，对数平均未命中率（MR）分别为3.97％和9.5％。如果使用我们的分类器对提案进行重新分类，则MR分别为3.38％和8.4％。可以进一步提高检测性能，特别是对于小规模的行人。在Caltech和CityPersons上进行的实验表明，在合理的设置下，MSAF可以显着提高行人检测性能，对数平均未命中率（MR）分别为3.97％和9.5％。如果使用我们的分类器对提案进行重新分类，则MR分别为3.38％和8.4％。可以进一步提高检测性能，特别是对于小规模的行人。在Caltech和CityPersons上进行的实验表明，在合理的设置下，MSAF可以显着提高行人检测性能，对数平均未命中率（MR）分别为3.97％和9.5％。如果使用我们的分类器对提案进行重新分类，则MR分别为3.38％和8.4％。可以进一步提高检测性能，特别是对于小规模的行人。