Research on Spider Recognition Technology Based on Transfer Learning and Attention Mechanism

Abstract

Methods such as transfer learning and attention mechanisms play an important role in small-sample image classification tasks. However, the conventional transfer method retains too much prior knowledge of the source domain and cannot learn the feature information of the target domain well. At the same time, it is difficult for the neural network model to find discriminative features and locate key feature regions, and it is easily interfered with by information such as complex backgrounds. Spiders usually appear symmetrical, but they are not perfectly symmetrical. How to accurately classify spider images depends on how to make the model focus on the key features for recognizing spiders in these symmetrical and asymmetrical regions. In view of the above problems, in this paper, we propose ECSM-ResNet-50, a model for small-sample spider image classification. The model fuses channel and spatial information and pays attention to the correlation between different locations in the input data. The Efficient Channel Attention (ECA) mechanism and the spatial attention mechanism were added to the model, and the self-attention mechanism was added to the end of the model. ECSM-ResNet-50 was constructed and trained on a small-sample spider data set (SPIDER9-IMAGE) using a layer-by-layer fine-tuning transfer learning strategy. Compared with ResNet-50, ECSM-ResNet-50 improved the average accuracy of nine species of spider recognition by 1.57% to 90.25%. This study contributes to the field of small-sample image recognition.

Keywords

• deep learning
• spider identification
• transfer learning
• fine-tuning
• attention mechanism