GENERIC OBJECT DETECTION IN IMAGES

摘要

Neural networks for object detection in images are used with a spatial pyramid pooling (SPP) layer. Using the SPP network structure, a fixed-length representation is generated regardless of image size and scale. The feature maps are computed from the entire image once, and the features are pooled in arbitrary regions (sub-images) to generate fixed- length representations for training the detectors. Thus, repeated computation of the convolutional features is avoided while accuracy is enhanced.