Detecting small objects is a challenging job for the single-shot multibox detector (SSD) model due to the limited information contained in features and complex background interference. Here, we increased the performance of the SSD for detecting target objects with small size by enhancing detection features with contextual information and introducing a segmentation mask to eliminate background regions. The proposed model is referred to as a “guided SSD” (Mask-SSD) and includes two branches: a detection branch and a segmentation branch. We created a feature-fusion module to allow the detection branch to exploit contextual information for feature maps with large resolution, with the segmentation branch primarily built with atrous convolution to provide additional contextual information to the detection branch. The input of the segmentation branch was also the output of the detection branch, and output segmentation features were fused with detection features in order to classify and locate target objects. Additionally, segmentation features were applied to generate the mask, which was utilized to guide the detection branch to find objects in potential foreground regions. Evaluation of Mask-SSD on the Tsinghua-Tencent 100K and Caltech pedestrian datasets demonstrated its effectiveness at detecting small objects and comparable performance relative to other state-of-the-art methods.

由于功能中包含的信息有限以及复杂的背景干扰，对于单发多盒检测器（SSD）模型而言，检测小物体是一项艰巨的任务。在这里，我们通过利用上下文信息增强检测功能并引入分割掩码以消除背景区域，从而提高了SSD用于检测小尺寸目标物体的性能。所提出的模型称为“引导SSD”（Mask-SSD），包括两个分支：检测分支和分段分支。我们创建了一个特征融合模块，以允许检测分支利用高分辨率的特征图利用上下文信息，而分割分支主要是通过无规则卷积构建的，以向检测分支提供其他上下文信息。分割分支的输入也是检测分支的输出，并且将输出分割特征与检测特征融合在一起，以对目标对象进行分类和定位。此外，应用了分割功能以生成遮罩，该遮罩用于引导检测分支以找到潜在前景区域中的对象。在清华腾讯100K和加州理工学院行人数据集上对Mask-SSD的评估表明，与其他最新方法相比，Mask-SSD在检测小物体和可比性能方面具有有效性。用来引导检测分支在潜在前景区域中找到对象。在清华腾讯100K和加州理工学院行人数据集上对Mask-SSD的评估表明，与其他最新方法相比，Mask-SSD在检测小物体和可比性能方面具有有效性。用来引导检测分支在潜在前景区域中找到对象。在清华腾讯100K和加州理工学院行人数据集上对Mask-SSD的评估表明，与其他最新方法相比，Mask-SSD在检测小物体和可比性能方面具有有效性。