Channel pruning is one of the important methods for deep model compression. Most of existing pruning methods mainly focus on classification. Few of them conduct systematic research on object detection. However, object detection is different from classification, which requires not only semantic information but also localization information. In this paper, based on DCP \cite{zhuang2018discrimination} which is state-of-the-art pruning method for classification, we propose a localization-aware auxiliary network to find out the channels with key information for classification and regression so that we can conduct channel pruning directly for object detection, which saves lots of time and computing resources. In order to capture the localization information, we first design the auxiliary network with a contextual ROIAlign layer which can obtain precise localization information of the default boxes by pixel alignment and enlarges the receptive fields of the default boxes when pruning shallow layers. Then, we construct a loss function for object detection task which tends to keep the channels that contain the key information for classification and regression. Extensive experiments demonstrate the effectiveness of our method. On MS COCO, we prune 70\% parameters of the SSD based on ResNet-50 with modest accuracy drop, which outperforms the-state-of-art method.

中文翻译：

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用于对象检测的本地化感知通道修剪

通道剪枝是深度模型压缩的重要方法之一。大多数现有的剪枝方法主要集中在分类上。他们中很少有人对目标检测进行系统研究。然而，物体检测不同于分类，它不仅需要语义信息，还需要定位信息。在本文中，基于最先进的分类剪枝方法 DCP \cite{zhuang2018discrimination}，我们提出了一个定位感知辅助网络来找出具有分类和回归关键信息的通道，以便我们可以直接对目标检测进行通道剪枝，节省大量时间和计算资源。为了捕获定位信息，我们首先设计了一个带有上下文 ROIAlign 层的辅助网络，它可以通过像素对齐获得默认框的精确定位信息，并在修剪浅层时扩大默认框的感受野。然后，我们为目标检测任务构建了一个损失函数，它倾向于保留包含分类和回归关键信息的通道。大量实验证明了我们方法的有效性。在 MS COCO 上，我们基于 ResNet-50 修剪了 SSD 的 70% 参数，精度下降适度，这优于最先进的方法。我们为目标检测任务构建了一个损失函数，它倾向于保留包含分类和回归关键信息的通道。大量实验证明了我们方法的有效性。在 MS COCO 上，我们基于 ResNet-50 修剪了 SSD 的 70% 参数，精度下降适度，这优于最先进的方法。我们为目标检测任务构建了一个损失函数，它倾向于保留包含分类和回归关键信息的通道。大量实验证明了我们方法的有效性。在 MS COCO 上，我们基于 ResNet-50 修剪了 SSD 的 70% 参数，精度下降适度，这优于最先进的方法。