TransAttention U-Net for Semantic Segmentation of Poppy

Abstract

This work represents a new attempt to use drone aerial photography to detect illegal cultivation of opium poppy. The key of this task is the precise segmentation of the poppy plant from the captured image. To achieve segmentation mask close to real data, it is necessary to extract target areas according to different morphological characteristics of poppy plant and reduce complex environmental interference. Based on RGB images, poppy plants, weeds, and background regions are separated individually. Firstly, the pixel features of poppy plant are enhanced using a hybrid strategy approach to augment the too-small samples. Secondly, the U-Shape network incorporating the self-attention mechanism is improved to segment the enhanced dataset. In this process, the multi-head self-attention module is enhanced by using relative position encoding to deal with the special morphological characteristics between poppy stem and fruit. The results indicated that the proposed method can segmented out the poppy plant precisely.

An Improved UNet Lightweight Network for Semantic Segmentation of Weed Images in Corn Fields

“…Segnet is a lightweight segmentation network for real-time image segmentation tasks in environments with limited computational resources, and variants of it are also used in many applications [10][11][12][13]. At the same time, an excellent network architecture can improve the model's ability to understand data, generalize, and adapt, thereby achieving better performance in various tasks [14][15][16].…”

Fast Opium Poppy Detection in Unmanned Aerial Vehicle (UAV) Imagery Based on Deep Neural Network

“…Luo et al. [7] employed a semantic segmentation model for pixel-level extraction of poppy regions and proposed a TransAttention U-Net model. However, poppies at various growth stages bear a striking resemblance to vegetation in terms of shape, rendering existing object detection methods potentially inadequate for accurately identifying poppies.…”