Location of safflower filaments picking points in complex environment based on improved Yolov5 algorithm

IF 7.7 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Computers and Electronics in Agriculture Pub Date : 2024-11-27 DOI:10.1016/j.compag.2024.109463

Xiaorong Wang , Jianping Zhou , Yan Xu , Chao Cui , Zihe Liu , Jinrong Chen

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引用次数: 0

Abstract

Mechanized safflower harvesting is prone to inaccurate recognition and positioning of safflower filaments, which is influenced by complex environmental factors such as occlusion, lighting, and challenges related to small targets and small samples. To solve this problem, we improved on the Yolov5 algorithm model and developed a two-stage recognition and positioning approach named Yolov5-ABBM. A safflower dataset was established to classify safflower filaments based on their maturity levels. The Swin Transformer attention mechanism was incorporated to improve the feature-extraction capability of the algorithm model, particularly for small samples and small targets. A geometric operation algorithm based on Bbox and Mask (ABBM) was developed to enhance the positioning speed and minimize missed recognition when locating safflower-filament picking points. Experimental results show that the improved model achieved a recognition precision improvement of 5.8% and 7.9% based on Bbox and Mask, respectively, and exhibited a significant enhancement of 15.3% and 19.4% for small samples. The positioning precision reached 98.19%, with an average positioning running time of 0.018 s per frame image. The improved model demonstrated superior accuracy and positioning speed compared with other algorithm models. The results show that the improved model could accurately identify and locate safflower-filament picking points, particularly for small samples, thereby offering technical support for efficient mechanized safflower harvesting.

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基于改进的 Yolov5 算法的复杂环境中红花花丝采摘点的定位

机械化红花收获容易出现红花花丝识别和定位不准确的问题，这受到遮挡、光照等复杂环境因素的影响，以及与小目标和小样本相关的挑战。为解决这一问题，我们对 Yolov5 算法模型进行了改进，开发了一种名为 Yolov5-ABBM 的两阶段识别和定位方法。我们建立了一个红花数据集，根据成熟度对红花花丝进行分类。为了提高算法模型的特征提取能力，特别是针对小样本和小目标的特征提取能力，研究人员采用了 Swin Transformer 注意机制。开发了一种基于 Bbox 和 Mask（ABBM）的几何运算算法，以提高定位速度，并在定位红花丝采摘点时尽量减少漏识。实验结果表明，在 Bbox 和 Mask 的基础上，改进模型的识别精度分别提高了 5.8% 和 7.9%，对于小样本的识别精度则分别显著提高了 15.3% 和 19.4%。定位精度达到 98.19%，每帧图像的平均定位时间为 0.018 秒。与其他算法模型相比，改进后的模型在精确度和定位速度方面都表现出了更高的水平。结果表明，改进后的模型能够准确识别和定位红花纤丝采摘点，尤其是小样本的采摘点，从而为高效的机械化红花收获提供了技术支持。

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来源期刊

Computers and Electronics in Agriculture 工程技术-计算机：跨学科应用

CiteScore

15.30

自引率

14.50%

发文量

800

审稿时长

62 days

期刊介绍： Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.