面向复杂田间收获作业的轻量化三七目标检测方法
S220.1; 针对目前三七检测算法在复杂田间收获工况下检测精度低、模型复杂度大、移动端部署难等问题,该研究提出一种基于YOLOv5s的轻量化三七目标检测方法.首先,采用GSConv卷积方法替换原始颈部网络的传统卷积,引入Slim-neck轻量级颈部网络,降低了模型复杂度,同时提升了模型精度;其次,使用ShuffleNetv2轻量型特征提取网络对主干网络进行轻量化改进,提升了模型实时检测性能,并采用角度惩罚度量的损失(SIoU)优化边界框损失函数,提升了轻量化后的模型精度和泛化能力.试验结果表明,改进后的PN-YOLOv5s模型参数量、计算量、模型大小分别为原YOLOv5s模型的46.65％、...
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| Published in | 农业工程学报 Vol. 40; no. 8; pp. 133 - 143 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | Chinese |
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昆明理工大学现代农业工程学院,昆明 650500
01.04.2024
昆明理工大学机电工程学院,昆明 650500%黑龙江佳木斯汤原县农业技术推广中心,佳木斯 154000 云南省高校中药材机械化工程研究中心,昆明 650500%云南省高校中药材机械化工程研究中心,昆明 650500 |
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| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.11975/j.issn.1002-6819.202401138 |
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| Abstract | S220.1; 针对目前三七检测算法在复杂田间收获工况下检测精度低、模型复杂度大、移动端部署难等问题,该研究提出一种基于YOLOv5s的轻量化三七目标检测方法.首先,采用GSConv卷积方法替换原始颈部网络的传统卷积,引入Slim-neck轻量级颈部网络,降低了模型复杂度,同时提升了模型精度;其次,使用ShuffleNetv2轻量型特征提取网络对主干网络进行轻量化改进,提升了模型实时检测性能,并采用角度惩罚度量的损失(SIoU)优化边界框损失函数,提升了轻量化后的模型精度和泛化能力.试验结果表明,改进后的PN-YOLOv5s模型参数量、计算量、模型大小分别为原YOLOv5s模型的46.65％、34.18％和48.75％,检测速度提升了 1.2倍,A值较原始模型提升了 0.22个百分点,平均精度均值达到了 94.20％,较原始模型低 0.6 个百分点,与 SSD、Faster R-CNN、YOLOv4-tiny、YOLOv7-tiny 和 YOLOv8s模型相比能够更好地平衡检测精度与速度,检测效果更好.台架试验测试结果表明,4种输送分离作业工况下三七目标检测的准确率达90％以上,F1值达86％以上,平均精度均值达87％以上,最低检测速度为105帧/s,实际收获工况下模型的检测性能良好,可为后续三七收获作业质量实时监测与精准分级输送提供技术支撑. |
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| AbstractList | S220.1; 针对目前三七检测算法在复杂田间收获工况下检测精度低、模型复杂度大、移动端部署难等问题,该研究提出一种基于YOLOv5s的轻量化三七目标检测方法.首先,采用GSConv卷积方法替换原始颈部网络的传统卷积,引入Slim-neck轻量级颈部网络,降低了模型复杂度,同时提升了模型精度;其次,使用ShuffleNetv2轻量型特征提取网络对主干网络进行轻量化改进,提升了模型实时检测性能,并采用角度惩罚度量的损失(SIoU)优化边界框损失函数,提升了轻量化后的模型精度和泛化能力.试验结果表明,改进后的PN-YOLOv5s模型参数量、计算量、模型大小分别为原YOLOv5s模型的46.65％、34.18％和48.75％,检测速度提升了 1.2倍,A值较原始模型提升了 0.22个百分点,平均精度均值达到了 94.20％,较原始模型低 0.6 个百分点,与 SSD、Faster R-CNN、YOLOv4-tiny、YOLOv7-tiny 和 YOLOv8s模型相比能够更好地平衡检测精度与速度,检测效果更好.台架试验测试结果表明,4种输送分离作业工况下三七目标检测的准确率达90％以上,F1值达86％以上,平均精度均值达87％以上,最低检测速度为105帧/s,实际收获工况下模型的检测性能良好,可为后续三七收获作业质量实时监测与精准分级输送提供技术支撑. |
| Abstract_FL | Intelligent harvesting is often required for object detection under complex field conditions,especially in the process of the real-time monitoring of harvesting quality and accurate grading conveyor.Taking Panax Notoginseng as the research plant,this study aims to propose lightweight object detection using YOLOv5s.The complex field conditions included the large variations in the light intensity,the difficulty in separating the roots from the soil,easy entanglement of roots,variable lifting speed,vibration amplitude,and frequency.The optimal model was also obtained with the high accuracy,and low complexity of a large model suitable for the deployment of mobile terminals.Firstly,a sample dataset was collected from the Panax Notoginseng in the complex field.The influence parameters of transportation and separation were also determined for the complex root-soil system;Secondly,real-time detection was realized under complex field conditions.Slim-neck lightweight neck network was introduced into the lightweight convolution of GSConv.The original SPPF feature fusion module was retained,while the ShuffleNetv2 lightweight feature extraction network was used to improve the original backbone network,which greatly reduced the model complexity with the model accuracy;Finally,the loss function with angular penalty metric(SCYLLA-IoU,SIoU)was used to optimize the bounding box loss function,in order to enhance the detection accuracy and generalization performance of the lightweight improved model.Ablation experiments were carried out to verify three improvement strategies,namely the Slim-neck neck feature extraction network,ShuffleNetv2 backbone feature extraction network,and SIoU bounding box loss function.The experimental results showed that the improved lightweight model(PN-YOLOv5s)had 3.27× 106 M parameters,5.4 G computational complexity,6.85 MB weight size,and a detection speed of 108 frames per second.The number of parameters and weight size were approximately half of the original YOLOv5s,while the computational complexity was about one-third of the original model,and the detection speed increased by 1.2 times.Additionally,the precision of the improved model reached 93.15％,which was almost the same as the original model.The recall reached 89.46％with an improvement of 0.48 percent points,compared with the original model.TheF1score reached 91.27％with an improvement of 0.22 percent points.The mean average precision reached 94.20％,only 0.6 percent points lower than the original.Compared with the mainstream SSD,Faster R-CNN,YOLOv4-tiny,YOLOv7-tiny,and YOLOv8s models,the improved lightweight model greatly reduced the complexity of the model,indicating better overall performance,in terms of model accuracy and real-time detection.The best performance was achieved in the actual harvesting,indicating the stronger robustness more suitable for deployment into mobile terminals.The bench tests showed that when the lifting inclination angle and vibration amplitude remained unchanged,the detection performance of the improved model decreased with the increase in lifting speeds and vibration frequencies.On the whole,the target detection of Panax Notoginseng was achieved with a precision of over 90％,an F1 score of over 86％,and a mean average precision of over 87％under four conditions of conveying and separation operating.There was little difference in the detection speed.But the improved model can fully meet the requirements of real-time detection under actual harvesting conditions.The finding can provide technical support to the subsequent monitoring of harvesting quality and adaptive grading conveyor in the Panax Notoginseng combine harvesters. |
| Author | 王法安 张兆国 房汉玉 解开婷 何忠平 |
| AuthorAffiliation | 昆明理工大学现代农业工程学院,昆明 650500;云南省高校中药材机械化工程研究中心,昆明 650500%云南省高校中药材机械化工程研究中心,昆明 650500;昆明理工大学机电工程学院,昆明 650500%黑龙江佳木斯汤原县农业技术推广中心,佳木斯 154000 |
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| Author_FL | XIE Kaiting WANG Fa'an HE Zhongping ZHANG Zhaoguo FANG Hanyu |
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| DocumentTitle_FL | Lightweight object detection method for Panax Notoginseng in complex field harvesting |
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| Keywords | 复杂收获作业工况 三七 YOLOv5s Panax Notoginseng 目标检测 complex harvesting conditions object detection 神经网络 neural network 轻量化 lightweight |
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| Title | 面向复杂田间收获作业的轻量化三七目标检测方法 |
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