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

文献类型： 外文期刊

作者： Yu, Caili 1 ; Mai, Yanheng 2 ; Yang, Caijuan 3 ; Zheng, Jiaqi 2 ; Liu, Yongxin 4 ; Yu, Chaoran 5 ;

作者机构： 1.Shanwei Inst Technol, Ctr Intelligent Percept & Internet Things Res, Shanwei 516600, Peoples R China

2.South China Agr Univ, Coll Elect Engn, Coll Artificial Intelligence, Guangzhou 510642, Peoples R China

3.Natl S&T Innovat Ctr Modern Agr Ind, Guangzhou 510520, Peoples R China

4.Embry Riddle Aeronaut Univ, Dept Math, Daytona Beach, FL 32114 USA

5.Guangdong Acad Agr Sci, Vegetable Res Inst, Guangdong Key Lab New Technol Res Vegetables, Guangzhou 510640, Peoples R China

关键词： precision agriculture; instance segmentation; drone; deep learning; computer vision

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）

ISSN：

年卷期： 2024 年 14 卷 12 期

页码：

收录情况： SCI

摘要： 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.

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