  Deep Convolutional Neural Network (DCNN) and Transformer have achieved
remarkable successes in image recognition. However, their performance in
fine-grained image recognition is still difficult to meet the requirements of
actual needs. This paper proposes a Sequence Random Network (SRN) to enhance
the performance of DCNN. The output of DCNN is one-dimensional features. This
one-dimensional feature abstractly represents image information, but it does
not express well the detailed information of image. To address this issue, we
use the proposed SRN which composed of BiLSTM and several Tanh-Dropout blocks
(called BiLSTM-TDN), to further process DCNN one-dimensional features for
highlighting the detail information of image. After the feature transform by
BiLSTM-TDN, the recognition performance has been greatly improved. We conducted
the experiments on six fine-grained image datasets. Except for FGVC-Aircraft,
the accuracies of the proposed methods on the other datasets exceeded 99%.
Experimental results show that BiLSTM-TDN is far superior to the existing
state-of-the-art methods. In addition to DCNN, BiLSTM-TDN can also be extended
to other models, such as Transformer.
