%0 Journal Article
%T A scene recognition algorithm based on deep residual network
%A Hu Yahong
%A Mao Jiafa
%A Sheng Weiguo
%A Wang Weifeng
%J Systems Science & Control Engineering
%D 2019
%R https://doi.org/10.1080/21642583.2019.1647576
%X Scene recognition is quite important in the field of robotics and computer vision. Aiming at providing high performance and universality of feature extraction, a convolutional neural network-based scene recognition model entitled Scene-RecNet is proposed. To reduce parameter space and improve the feature quality, deep residual network is introduced as the feature extractor. A feature adjustment layer composed of a convolutional layer and a fully connected layer is added after the feature extractor to further synthesize and compress the extracted features. Migration learning-based ¡®pre-training and fine-tuning¡¯ mode is used to train Scene-RecNet. The feature extractor is pre-trained by ImageNet, and the overall network performance is fine-tuned on specific data sets. Experiments show that comparing with other algorithms, the features obtained by Scene-RecNet have high generality and robustness, and Scene-RecNet can provide better scene classification accuracy rate
%U https://www.tandfonline.com/doi/full/10.1080/21642583.2019.1647576