Deep Neural Network Pruning Based Two-Stage Remote Sensing Image Object Detection

doi:10.12068/j.issn.1005-3026.2019.02.005

Abstract

Abstract: In the object detection of high-resolution remote-sensing images， affected by cloud， light， complex background， noise and other factors， the existing object detection method has high false alarm， low speed and low precision. So we propose a two-stage object detection method based on deep pruning. First， we propose deep pruning， and then based on the deep pruning we propose an algorithm that learns region proposal network automatically and an algorithm that we train classification network with optimizing training method. We then apply the two algorithms to convolutional neural network and get a two-stage object detection model. The experiment result shows that our method has a certain improvement on precision and speed compared with the state-of-the-art method.

Key words: computer vision, object detection, high-resolution remote sensing image, deep learning, convolutional neural network

CLC Number:

TP391

WANG Sheng-sheng， WANG Meng， WANG Guang-yao. Deep Neural Network Pruning Based Two-Stage Remote Sensing Image Object Detection[J]. Journal of Northeastern University Natural Science, 2019, 40(2): 174-179.

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