Multiple spatial residual network for object detection

Abstract 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.