A multi-branch dual attention segmentation network for epiphyte drone images

Acquiring images of epiphytes growing on trees using Unmanned Aerial Vehicles (UAVs) enables botanists to efficiently collect data on these important plant species. Despite the advantages offered by UAVs, challenges such as complex backgrounds, uneven lighting inside the tree canopy, and accessibility issues hinder the acquisition of quality images, resulting in acquiring images datasets of heterogenous quality. AI/Deep Learning algorithms can be used to segment target plants in these images for selecting sampling locations. Existing DL models require large volume of data for training, and they tend to prioritize local features over global ones, impacting segmentation accuracy, particularly on smaller, heterogeneous quality image datasets. To overcome these limitations, we propose a multi-branch dual attention segmentation network designed to effectively handle small datasets with heterogeneous quality. The proposed network incorporates dedicated branches for extracting both global and local features, utilizing spatial and channel attention mechanisms to focus on important regions. Through a fusion process and a decoder with crossed fusion technique, this network effectively combines and enhances features from multiple branches, resulting in improved segmentation performance. Output obtained from the trained model demonstrated major improvements in predicting the boundary regions and class labels, even in close-range, low-light, and zoomed/cropped images. The average Intersection over Union (IoU) scores of the trained model was 5% higher for images acquired close range, 48% higher for images in low-light conditions, and 68% higher for zoomed/cropped images when compared to those obtained from TransUnet, a state-of-the-art vision transformer model trained on epiphyte dataset. The proposed network can be used for segmenting epiphytes in images of heterogeneous quality as well as identifying targets in images acquired in domains such as agriculture and forestry.