A two-stream network with complementary feature fusion for pest image classification

Pests are diverse and the available datasets often contain an uneven number of examples for different pests (a.k.a., the long-tail distribution). This poses a great challenge to learning-based classification methods, especially deep networks, and often leads to degraded performance, especially for the minority (tail) classes. This paper presents a deep learning integration architecture based on decoupling training and fusion learning, which integrates different models with complementary performance on pest datasets with a long-tailed distribution to improve the overall classification performance of pests. A deep neural network is designed that fuses two complementary deep learning models at the feature level, which consists of a convolution neural network (ConvNeXt) and a Swin Transformer model for decoupling training. Experiments are conducted using three datasets (d0, insect, and IP102), and evaluation on accuracy, recall, and F1-Score is reported. For the large-scale pest dataset with long-tailed distribution IP102, the accuracy achieves 76.1%, which outperforms the state-of-the-art methods. In addition, the accuracy for d0 and insect datasets are 98.5% and 92.3%, respectively.