Improved YOLOv5 Method for Detecting Growth Status of Apple Flowers

doi:10.3778/j.issn.1002-8331.2107-0523

Abstract

Abstract: Aiming at the problem that it is difficult for the existing target detection algorithm to detect the growth status of apple flowers in the complex environment of the orchard, an improved YOLOv5 method for detecting the growth status of apple flowers is proposed. The growth status of the flowers during the flowering period of the tree is tested. This method firstly improves the cross-stage local network module, adjusts the number of modules, and designs the backbone network with the collaborative attention module to improve model detection performance and reduce parameters. Secondly, this method combines the new detection scale and the split-based convolution operation to design a feature fusion network to improve the network feature fusion ability, and finally chooses CIoU as the loss function of the frame regression to achieve high-precision positioning. The improved algorithm is compared with the original YOLOv5 algorithm on a self-built data set. The experimental results show that the mAP of the algorithm in this paper reaches 0.922, which is 5.4 percentage points higher than that of YOLOv5. Compared with other mainstream algorithms, the detection accuracy is greatly improved, which proves the effectiveness of the algorithm.

Key words: YOLOv5, agricultural automatic monitoring, feature fusion, object detection

摘要： 针对现有目标检测算法难以在果园复杂环境下对苹果花朵生长状态进行高精度检测的问题，提出一种改进YOLOv5的苹果花朵生长状态检测方法，对花蕾、半开、全开、凋落四类苹果树开花期花朵生长状态进行检测。该方法对跨阶段局部网络模块进行改进，并调整模块数量，结合协同注意力模块设计主干网络，提高模型检测性能并减少参数。结合新的检测尺度与基于拆分的卷积运算设计特征融合网络，提升网络特征融合能力。选用CIoU作为边框回归的损失函数实现高精度的定位。将改进算法与原始YOLOv5算法在自建数据集上进行对比实验，结果表明，改进算法mAP达到0.922，比YOLOv5提高5.4个百分点，与其他主流算法相比检测精度有较大提升，证明了算法的有效性。

关键词: YOLOv5, 农业自动监测, 特征融合, 目标检测

YANG Qisheng, LI Wenkuan, YANG Xiaofeng, YUE Linxi, LI Haifang. Improved YOLOv5 Method for Detecting Growth Status of Apple Flowers[J]. Computer Engineering and Applications, 2022, 58(4): 237-246.

杨其晟, 李文宽, 杨晓峰, 岳琳茜, 李海芳. 改进YOLOv5的苹果花生长状态检测方法[J]. 计算机工程与应用, 2022, 58(4): 237-246.

References

[1] 邓颖，吴华瑞，朱华吉.基于实例分割的柑橘花朵识别及花量统计[J].农业工程学报，2020，36（7）：200-207.
DENG Y，WU H R，ZHU H J.Citrus flower identification and flower volume statistics based on instance segmentation[J].Transactions of the Chinese Society of Agricultural Engineering，2020，36（7）：200-207.
[2] SHARMA A，SHARMA V K，SRIVASTAVA D K.Overlapped flowers yield detection using computer-based interface[J].Perspectives in Science，2016，8：25-27.
[3] 刘双喜，孙林林，王震，等.复杂背景下苹果树花量模糊聚类准确估测模型[J].中国农机化学报，2017，38（8）：74-81.
LIU S X，SUN L L，WANG Z，et al.Fuzzy clustering accurate estimation model of apple tree flower volume under complex background[J].Chinese Journal of Agricultural Machinery Chemistry，2017，38（8）：74-81.
[4] DIAS P A，TABB A，MEDEIROS H.Apple flower detection using deep convolutional networks[J].Computers in Industry，2018，99：17-28.
[5] 郑玉龙，赵明.基于深度学习的自然环境下花朵识别[J].计算技术与自动化，2019（2）：114-118.
ZHENG Y L，ZHAO M.Flower recognition in natural environment based on deep learning[J].Computing Technology and Automation，2019（2）：114-118.
[6] WU D，LV S，JIANG M，et al.Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments[J].Computers and Electronics in Agriculture，2020，178：105742.
[7] HOU Q，ZHOU D，FENG J.Coordinate attention for efficient mobile network design[C]//Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition，2021：13713-13722.
[8] ZHANG Q，JIANG Z，LU Q，et al.Split to be slim：an overlooked redundancy in vanilla convolution[J].arXiv：2006.12085，2020.
[9] REN S Q，HE K M，GIRSHICK R，et al.Faster R-CNN：towards real-time object detection with region proposal networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2017，39（6）：1137-1149.
[10] CAI Z，VASCONCELOS N.Cascade R-CNN：delving into high quality object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：6154-6162.
[11] LIN T Y，GOYAL P，GIRSHICK R，et al.Focal loss for dense object detection[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision，2017：2980-2988.
[12] LIU W，ANGUELOV D，ERHAN D，et al.SSD：single shot multibox detector[C]//14th European Conference on Computer Vision.Cham：Springer，2016：21-37.
[13] DUAN K，BAI S，XIE L，et al.Centernet：keypoint triplets for object detection[C]//Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision，2019：6569-6578.
[14] BOCHKOVSKIY A，WANG C Y，LIAO H Y M.YOLOV4：optimal speed and accuracy of object detection[J].arXiv：2004.10934，2020.
[15] WANG C Y，LIAO H Y M，WU Y H，et al.CSPNet：a new backbone that can enhance learning capability of CNN[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops，2020：390-391.
[16] LIN T Y，DOLLáR P，GIRSHICK R，et al.Feature pyramid networks for object detection[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition，2017：2117-2125.
[17] LIU S，QI L，QIN H，et al.Path aggregation network for instance segmentation[C]//Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition，2018：8759-8768.
[18] HE K，ZHANG X，REN S，et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2015，37（9）：1904-1916.