Many residual network-based methods have been proposed to perform object detection. However, most of them may lead to overfitting or cannot perform well in small object detection and alleviate the problem of overfitting. We propose a multiple spatial residual network (MSRNet) for object detection. Particularly, our method is based on central point detection algorithm. Our proposed MSRNet employs a residual network as the backbone. The resulting features are processed by our proposed residual channel pooling module. We then construct a multi-scale feature transposed residual fusion structure consists of three overlapping stacked residual convolution modules and a transpose convolution function. Finally, we use the Center structure to process the high-resolution feature image for obtaining the final prediction detection result. Experimental results on PASCAL VOC dataset and COCO dataset confirm that the MSRNet has competitive accuracy compared with several other classical object detection algorithms, while providing a unified framework for training and reasoning. The MSRNet runs on GeForce RTX 2080Ti.

已经提出了许多基于残差网络的方法来执行目标检测。但是，它们中的大多数可能会导致过拟合或在小物体检测中表现不佳，从而缓解过拟合问题。我们提出了一种用于目标检测的多空间残差网络（MSRNet）。特别是，我们的方法基于中心点检测算法。我们提出的 MSRNet 采用残差网络作为主干。由此产生的特征由我们提出的残差通道池模块处理。然后我们构建了一个多尺度特征转置残差融合结构，由三个重叠的堆叠残差卷积模块和一个转置卷积函数组成。最后，我们使用Center结构对高分辨率特征图像进行处理，得到最终的预测检测结果。在 PASCAL VOC 数据集和 COCO 数据集上的实验结果证实，与其他几种经典的目标检测算法相比，MSRNet 具有具有竞争力的准确性，同时为训练和推理提供了统一的框架。MSRNet 在 GeForce RTX 2080Ti 上运行。