SizeNet: Object Recognition via Object Real Size-based convolutional networks

Inspired by the conclusion that human choose the visual cortex regions which corresponding to the real size of the object to analyze the features of the object, when realizing the objects in the real world. This paper presents a framework -- SizeNet which based on both the real sizes and the features of objects, to solve objects recognition problems. SizeNet was used for the objects recognition experiments on the homemade Rsize dataset, and compared with State-of-the-art Methods AlexNet, VGG-16, Inception V3, Resnet-18 DenseNet-121. The results show that SizeNet provides much higher accuracy rates for the objects recognition than the other algorithms. SizeNet can solve the two problems that correctly recognize the objects whose features are highly similar but the real sizes are obviously different from each other, and correctly distinguish the target object from the interference objects whose real sizes are obviously different from the target object. This is because SizeNet recognizes the object based not only the features, but also the real size. The real size of the object can help to exclude the interference object categories whose real size ranges do not match the real size of the object, which greatly reducing the object categories' number in the label set used for the downstream object recognition based on object features. SizeNet is of great significance to the study of interpretable computer vision. Our code and dataset will be made public.