IA-YOLO: A Vatica Segmentation Model Based on an Inverted Attention Block for Drone Cameras

Caili Yu, Yanheng Mai (), Caijuan Yang (), Jiaqi Zheng, Yongxin Liu and Chaoran Yu
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Caili Yu: Shanwei Institute of Technology, Center for Intelligent Perception and Internet of Things Research, Shanwei 516600, China
Yanheng Mai: College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
Caijuan Yang: National S&T Innovation Center for Modern Agricultural Industry, Guangzhou 510520, China
Jiaqi Zheng: College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China
Yongxin Liu: Department of Mathematics, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA
Chaoran Yu: Vegetable Research Institute, Guangdong Academy of Agricultural Sciences Guangdong Key Laboratory for New Technology Research of Vegetables, Guangzhou 510640, China

Agriculture, 2024, vol. 14, issue 12, 1-19

Abstract: The growing use of drones in precision agriculture highlights the needs for enhanced operational efficiency, especially in the scope of detection tasks, even in segmentation. Although the ability of computer vision based on deep learning has made remarkable progress in the past ten years, the segmentation of images captured by Unmanned Aerial Vehicle (UAV) cameras, an exact detection task, still faces a conflict between high precision and low inference latency. Due to such a dilemma, we propose IA-YOLO (Inverted Attention You Only Look Once), an efficient model based on IA-Block (Inverted Attention Block) with the aim of providing constructive strategies for real-time detection tasks using UAV cameras. The working details of this paper are outlined as follows: (1) We construct a component named IA-Block, which is integrated into the YOLOv8-seg structure as IA-YOLO. It specializes in pixel-level classification of UAV camera images, facilitating the creation of exact maps to guide agricultural strategies. (2) In experiments on the Vatica dataset, compared with any other lightweight segmentation model, IA-YOLO achieves at least a 3.3% increase in mAP (mean Average Precision). Further validation on diverse species datasets confirms its robust generalization. (3) Without overloading the complex attention mechanism and deeper and deeper network, a stem that incorporates efficient feature extraction components, IA-Block, still possess credible modeling capabilities.

Keywords: precision agriculture; instance segmentation; drone; deep learning; computer vision (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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