  The pests captured with imaging devices may be relatively small in size
compared to the entire images, and complex backgrounds have colors and textures
similar to those of the pests, which hinders accurate feature extraction and
makes pest identification challenging. The key to pest identification is to
create a model capable of detecting regions of interest (ROIs) and transforming
them into better ones for attention and discriminative learning. To address
these problems, we will study how to generate and update the ROIs via
multiscale cross-attention fusion as well as how to be highly robust to complex
backgrounds and scale problems. Therefore, we propose a novel ROI-aware
multiscale cross-attention vision transformer (ROI-ViT). The proposed ROI-ViT
is designed using dual branches, called Pest and ROI branches, which take
different types of maps as input: Pest images and ROI maps. To render such ROI
maps, ROI generators are built using soft segmentation and a class activation
map and then integrated into the ROI-ViT backbone. Additionally, in the dual
branch, complementary feature fusion and multiscale hierarchies are implemented
via a novel multiscale cross-attention fusion. The class token from the Pest
branch is exchanged with the patch tokens from the ROI branch, and vice versa.
The experimental results show that the proposed ROI-ViT achieves 81.81%,
99.64%, and 84.66% for IP102, D0, and SauTeg pest datasets, respectively,
outperforming state-of-the-art (SOTA) models, such as MViT, PVT, DeiT,
Swin-ViT, and EfficientNet. More importantly, for the new challenging dataset
IP102(CBSS) that contains only pest images with complex backgrounds and small
sizes, the proposed model can maintain high recognition accuracy, whereas that
of other SOTA models decrease sharply, demonstrating that our model is more
robust to complex background and scale problems.
