MS-YOLOv5: a lightweight algorithm for strawberry ripeness detection based on deep learning

IF 3.2 Q2 AUTOMATION & CONTROL SYSTEMS Systems Science & Control Engineering Pub Date : 2023-11-29 DOI:10.1080/21642583.2023.2285292
Fengqian Pang, Xi Chen
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Abstract

The existing ripeness detection algorithm for strawberries suffers from low detection accuracy and high detection error rate. Considering these problems, we propose an improvement method based on YOLOv5, named MS-YOLOv5. The first step is to reconfigure the feature extraction network of MS-YOLOv5 by replacing the standard convolution with the depth hybrid deformable convolution (Ms-MDconv). In the second step, a double cooperative attention mechanism (Bc-attention) is constructed and implemented in the CSP2 module to improve the feature representation in complex environments. Finally, the Neck section of MS-YOLOv5 has been enhanced to use the fast-weighted fusion of cross-scale feature pyramid networks (FW-FPN) to replace the CSP2 module. It not only integrates multi-scale target features but also significantly reduces the number of parameters. The method was tested on the strawberry ripeness dataset, the mAP reached 0.956, the FPS reached 76, and the model size was 7.44M. The mAP and FPS are 8.4 and 1.3 percentage higher than the baseline network, respectively. The model size is reduced by 6.28M. This method is superior to mainstream algorithms in detection speed and accuracy. The system can accurately identify the ripeness of strawberries in complex environments, which could provide technical support for automated picking robots.
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MS-YOLOv5:基于深度学习的草莓成熟度轻量级检测算法
现有的草莓成熟度检测算法存在检测精度低、检测错误率高的问题。考虑到这些问题,我们提出了一种基于 YOLOv5 的改进方法,命名为 MS-YOLOv5。第一步是重新配置 MS-YOLOv5 的特征提取网络,将标准卷积替换为深度混合可变形卷积(Ms-MDconv)。第二步,在 CSP2 模块中构建并实施了双重合作注意机制(Bc-attention),以改进复杂环境中的特征表示。最后,MS-YOLOv5 的 "颈"(Neck)部分进行了改进,使用跨尺度特征金字塔网络的快速加权融合(FW-FPN)取代了 CSP2 模块。它不仅整合了多尺度目标特征,还大大减少了参数数量。该方法在草莓成熟度数据集上进行了测试,mAP 达到 0.956,FPS 达到 76,模型大小为 7.44M。与基线网络相比,mAP 和 FPS 分别提高了 8.4 和 1.3 个百分点。模型大小减少了 6.28M。该方法在检测速度和准确性方面均优于主流算法。该系统能在复杂环境中准确识别草莓的成熟度,可为自动采摘机器人提供技术支持。
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来源期刊
Systems Science & Control Engineering
Systems Science & Control Engineering AUTOMATION & CONTROL SYSTEMS-
CiteScore
9.50
自引率
2.40%
发文量
70
审稿时长
29 weeks
期刊介绍: Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory
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