Feature maps extracted from different layers of a convolutional neural network possess varying-size receptive fields with respect to the input image. This hierarchical structure can be leveraged for multiscale object detection by designing a network where feature maps closer to the output layer, which have wider receptive fields, are responsible for detecting larger objects, while shallower feature maps detect smaller objects.