Combining Cascaded Network and Adversarial Network for Object Detection

Recognizing multi-scale objects and objects with occlusions is a key and difficult point of task in object detection. In order to detect objects with different sizes, the object detector usually uses the hierarchical structure of multi-scale feature map constructed by convolutional neural network (CNN). However, due to the small convolution layer of the bottom feature map, the top-down structure lacks the detailed information needed to capture the features of small object. The performance of these object detectors is limited. Therefore, based on the Faster R-CNN (region-convolutional neural network) framework, this paper proposes Collaborative R-CNN. This paper designs a cascaded network structure that integrates multi-scale feature maps to generate deeply fused feature information and thereby improving the ability to detect small objects. Moreover, the quantization in the RoIPooling process greatly limits the recognition ability of small objects. In order to further improve the robustness of the method, a multi-scale RoIAlign is designed to eliminate such quantization, and the ability of network to detect objects with different scales is improved by multi-scale pooling. Finally, this paper combines an adversarial network with the proposed network to generate training samples with occlusions, significantly improving the classification ability of the model, and robustness to detect occlusions. Experimental results for the PASCAL VOC 2012 and PASCAL VOC 2007 datasets demonstrate the superiority of proposed approach relative to several state-of-the-art approaches.