Research on improvement strategies for a lightweight multi-object weed detection network based on YOLOv5

IF 2.5 2区 农林科学 Q1 AGRONOMY
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引用次数: 0

Abstract

Traditional weed detection technology has several limitations, including low detection accuracy, substantial computational demands, and large-scale detection models. To meet the requirements of weed multi-target identification and portability, this study proposes the YOLO–WEED model for weed recognition. The proposed model has the following innovations: (1) The backbone standard convolution module in YOLOv5 was replaced by the lightweight MobileNetv3 network to simplify the network structure and reduce parameter complexity; (2) The addition of convolutional block attention module (CBAM) to the neck network enabled the model to focus on the most important features while filtering out noise and irrelevant information; (3) To further improve classification accuracy and reduce loss, the C2f module was employed to improve the C3 module in the neck network; and (4) During the model plot process, a coordinate variable was added in the box label to help the model accurately locate the weeds. In the study, six species of weeds and one crop were used as test subjects. After image enhancement techniques were used, ablation experiments were deployed. The experimental results indicated that the YOLO–WEED model achieved an average accuracy of 92.5% in identifying six types of weeds and one type of crop. The accuracies for each type of plant were 82.7%, 97.3%, 98.8%, 86%, 93.5%, 99.3% and 89.6%, respectively. The number of model parameters was reduced by 39.4% compared with YOLOv5s. Furthermore, the localisation, classification and object losses were reduced by 0.025, 0.005 and 0.014, respectively. The model optimisation and deployment of the Jetson mobile terminal for multi-target detection were realised, and the performance was better than six network models such as YOLOv5s.

基于 YOLOv5 的轻量级多目标杂草检测网络改进策略研究
传统的杂草检测技术存在检测精度低、计算量大、检测模型庞大等局限性。为了满足杂草多目标识别和可移植性的要求,本研究提出了杂草识别的 YOLO-WEED 模型。该模型具有以下创新之处:(1)用轻量级的 MobileNetv3 网络取代了 YOLOv5 中的骨干标准卷积模块,简化了网络结构,降低了参数复杂度;(2)在颈部网络中增加了卷积块注意模块(CBAM),使模型在过滤噪声和无关信息的同时,能够关注最重要的特征;(3) 为进一步提高分类精度并减少损失,采用 C2f 模块改进了颈部网络中的 C3 模块;以及 (4) 在模型绘图过程中,在方框标签中添加了坐标变量,以帮助模型准确定位杂草。本研究以六种杂草和一种作物为测试对象。在使用图像增强技术后,进行了消融实验。实验结果表明,YOLO-WEED 模型识别六种杂草和一种作物的平均准确率达到 92.5%。各类植物的准确率分别为 82.7%、97.3%、98.8%、86%、93.5%、99.3% 和 89.6%。与 YOLOv5s 相比,模型参数数量减少了 39.4%。此外,定位、分类和目标损失分别减少了 0.025、0.005 和 0.014。实现了用于多目标检测的 Jetson 移动终端的模型优化和部署,其性能优于 YOLOv5s 等六个网络模型。
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来源期刊
Crop Protection
Crop Protection 农林科学-农艺学
CiteScore
6.10
自引率
3.60%
发文量
200
审稿时长
29 days
期刊介绍: The Editors of Crop Protection especially welcome papers describing an interdisciplinary approach showing how different control strategies can be integrated into practical pest management programs, covering high and low input agricultural systems worldwide. Crop Protection particularly emphasizes the practical aspects of control in the field and for protected crops, and includes work which may lead in the near future to more effective control. The journal does not duplicate the many existing excellent biological science journals, which deal mainly with the more fundamental aspects of plant pathology, applied zoology and weed science. Crop Protection covers all practical aspects of pest, disease and weed control, including the following topics: -Abiotic damage- Agronomic control methods- Assessment of pest and disease damage- Molecular methods for the detection and assessment of pests and diseases- Biological control- Biorational pesticides- Control of animal pests of world crops- Control of diseases of crop plants caused by microorganisms- Control of weeds and integrated management- Economic considerations- Effects of plant growth regulators- Environmental benefits of reduced pesticide use- Environmental effects of pesticides- Epidemiology of pests and diseases in relation to control- GM Crops, and genetic engineering applications- Importance and control of postharvest crop losses- Integrated control- Interrelationships and compatibility among different control strategies- Invasive species as they relate to implications for crop protection- Pesticide application methods- Pest management- Phytobiomes for pest and disease control- Resistance management- Sampling and monitoring schemes for diseases, nematodes, pests and weeds.
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