Detecting Small Objects Using a Channel-Aware Deconvolutional Network

Detecting small objects is a challenging task due to their low resolution and noisy representation even using deep learning methods. In this paper, we propose a novel object detection method based on the channel-aware deconvolutional network (CADNet) for accurate small object detection. Specifically, we develop the channel-aware deconvolution (ChaDeConv) layer to exploit the correlations of feature maps in different channels across deeper layers, improving the recall rate of small objects at low additional computational costs. Following the ChaDeConv layer, the multiple region proposal sub-network (Multi-RPN) is employed to supervise and optimize multiple detection layers simultaneously to achieve better accuracy. The Multi-RPN module is only used in the training phase and does not increase the computation cost of the inference. In addition, we design a new anchor matching strategy based on the center point translation (CPTMatching) of anchors to select more extending anchors as positive samples in the training phase. The extensive experiments on the PASCAL VOC 2007/2012, MS COCO, and UAVDT datasets show that the proposed CADNet achieves state-of-the-art performance compared to the existing methods.