Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

Abstract

Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224 × 224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, "spatial pyramid pooling", to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-the-art classification results using a single full-image representation and no fine-tuning. The power of SPP-net is also significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method is 24-102 × faster than the R-CNN method, while achieving better or comparable accuracy on Pascal VOC 2007. In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our methods rank #2 in object detection and #3 in image classification among all 38 teams. This manuscript also introduces the improvement made for this competition.

Aircraft detection in remote sensing images based on saliency and convolution neural network

“…These algorithms are mainly classified into two groups: one is object detection method based on region proposal [58][59][60], which is a mainstream algorithm, e.g., RCNN [31], SPPNet [61], Fast-RCNN [34], Faster-RCNN [62], and MSRA recently proposes algorithm R-FCN [63]. The other is not using the region proposal method to detection, e.g., YOLO [64] and SSD [65].…”

“…Due to the fact that there is a large amount of overlap between these RoIs, redundant calculations result in inefficiencies. SSP-Net [61] and Fast-RCNN [34] propose a shared feature method that is extracted only one time for the whole image for this problem. And then, about 2000 RoIs are mapped according to their location information to the feature vector of the whole image to obtain the features of each RoI, so it greatly improves the speed of calculation because the feature extraction calculations of different RoI can be shared.…”

Ear Detection under Uncontrolled Conditions with Multiple Scale Faster Region-Based Convolutional Neural Networks

“…However, because it performs a ConvNet for each object proposal, the time spent on computing region proposals and features (13s/image on a Graphics Processing Unit (GPU) or 53s/image on a CPU) cannot be ignored for an object detection system. Inspired by the Spatial pyramid pooling networks (SPPnets) [40], Girshick [34] proposed Fast R-CNN to speed up R-CNN by sharing computation. The network processed all the images with a CNN to produce a conv feature map.…”

An Efficient and Robust Integrated Geospatial Object Detection Framework for High Spatial Resolution Remote Sensing Imagery

“…High spatial resolution (HSR) remote sensing imaging sensors can now acquire aerial and satellite images with abundant detail and complex spatial structural information, which can be used in a wide range of civil and engineering applications, such as segmentation [4], scene annotation [5], object detection [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] (e.g., airplane detection [6,12], urban area detection [13], vehicle detection [21,22]), scene classification and recognition [23][24][25][26][27], etc. Differing from natural imagery obtained by the camera on the ground from a horizontal view, HSR remote sensing imagery is obtained by satellite-borne or space-borne sensors from a top-down view, which is an approach that can be easily influenced by weather and illumination conditions.…”