CasNet: Cascaded Architecture for Visual Object Detection Working with Existing CNNs

Imbalanced samples composed of limited number of positive samples corresponding to objects and huge number of negative samples extracted from background regions reduces the accuracy of visual object detection. To solve this problem this paper proposes a novel convolutional neural network named “CasNet”. CasNet introduces cascade structure that is used for rapid and accurate object detector in order to reduce the number of negative samples inputted to a main network for object detection. The CasNet becomes a cascade stage when it is attached to a layer of existing convolutional neural networks to construct cascaded classifier. Each stage composed of a CasNet performs two-class classification to reject easy negatives corresponding to background regions. By this early rejection of easy negatives, a main network can be trained to classify more complex samples. Experimental results using a dataset created from the PASCAL VOC2012 dataset showed that higher accuracy was obtained at less training iterations if CasNets were attached to VGG16 appropriately.