计算机与控制工程 |
|
|
|
|
基于改进YOLOv5的枸杞虫害检测 |
杜丁健1( ),高遵海1,*( ),陈倬2 |
1. 武汉轻工大学 数学与计算机学院,湖北 武汉,430048 2. 武汉轻工大学 管理学院,湖北 武汉,430048 |
|
Wolfberry pest detection based on improved YOLOv5 |
Dingjian DU1( ),Zunhai GAO1,*( ),Zhuo CHEN2 |
1. School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430048, China 2. School of Management, Wuhan Polytechnic University, Wuhan 430048, China |
1 |
陈磊, 刘立波, 王晓丽. 2020年宁夏枸杞虫害图文跨模态检索数据集[J]. 中国科学数据(中英文网络版), 2022, 7(3): 149−156. CHEN Lei, LIU Libo, WANG Xiaoli. A dataset of image-text cross-modal retrieval of Lycium barbarum pests in Ningxia in 2020 [J]. China Scientific Data . 2022, 7(3): 149−156.
|
2 |
王云露. 基于深度迁移学习的苹果病害识别方法研究[D]. 泰安: 山东农业大学, 2022: 2−14. WANG Yunlu. Apple disease identification method based on deep transfer learning [D]. Tai’an: Shandong Agricultural University, 2022: 2−14.
|
3 |
胡林龙. 基于图像处理的甘蓝型油菜的虫害程度与识别的研究[D]. 武汉: 武汉轻工大学, 2020: 10−45. HU Linlong. Study on pest degree and recognition of brassica napus based on image processing [D]. Wuhan: Wuhan Polytechnic University, 2020: 10−45.
|
4 |
EBRAHIMI M A, KHOSHTAGHAZA M H, MINAEI S, et al Vision-based pest detection based on SVM classification method[J]. Computers and Electronics in Agriculture, 2017, 137: 52- 58
doi: 10.1016/j.compag.2017.03.016
|
5 |
WEN C, CHEN H, MA Z, et al Pest-YOLO: a model for large-scale multi-class dense and tiny pest detection and counting[J]. Frontiers in Plant Science, 2022, 13: 973985
doi: 10.3389/fpls.2022.973985
|
6 |
BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection [EB/OL]. (2020−04−23)[2023−06−22]. https://arxiv.org/pdf/2004.10934.
|
7 |
王金星, 马博, 王震, 等 基于改进Mask R-CNN的苹果园害虫识别方法[J]. 农业机械学报, 2023, 54 (6): 253- 263 WANG Jinxing, MA Bo, WANG Zhen, et al Pest identification method in apple orchard based on improved Mask R-CNN[J]. Transactions of the Chinese Society of Agricultural Machinery, 2023, 54 (6): 253- 263
doi: 10.6041/j.issn.1000-1298.2023.06.026
|
8 |
HE K, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN [C]// Proceedings of the IEEE International Conference on Computer Vision . Venice: IEEE, 2017: 2961−2969.
|
9 |
WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module [C]// Proceedings of the European Conference on Computer Vision . Munich: Springer, 2018: 3−19.
|
10 |
XIE S, GIRSHICK R, DOLLÁR P, et al. Aggregated residual transformations for deep neural networks [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Honolulu: IEEE, 2017: 1492−1500.
|
11 |
王卫星, 刘泽乾, 高鹏, 等 基于改进YOLO v4的荔枝病虫害检测模型[J]. 农业机械学报, 2023, 54 (5): 227- 235 WANG Weixing, LIU Zeqian, GAO Peng, et al Detection of litchi diseases and insect pests based on improved YOLO v4 model[J]. Transactions of the Chinese Society of Agricultural Machinery, 2023, 54 (5): 227- 235
doi: 10.6041/j.issn.1000-1298.2023.05.023
|
12 |
HAN K, WANG Y, TIAN Q, et al. GhostNet: more features from cheap operations [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Seattle: IEEE, 2020: 1580−1589.
|
13 |
苏虹. 枸杞病虫害识别方法的研究与设计[D]. 银川: 宁夏大学, 2019: 1−2. SU Hong. Research and design of recognition algorithm for wolfberry pests and diseases [D]. Yinchuan: Ningxia University, 2019: 1−2.
|
14 |
REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas: IEEE, 2016: 779−788.
|
15 |
JOCHER G. YOLOv5: minor version 6.0 [EB/OL]. (2021−10−12) [2023−06−22]. https://github.com/ultralytics/yolov5/releases/tag/v6.0.
|
16 |
LI J, XIA X, LI W, et al. Next-ViT: next generation vision transformer for efficient deployment in realistic industrial scenarios [EB/OL]. (2022−08−16)[2023−06−22]. https://arxiv.org/pdf/2207.05501.
|
17 |
CHEN J, KAO S H, HE H, et al. Run, don’t walk: chasing higher FLOPS for faster neural networks [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Vancouver: IEEE, 2023: 12021−12031.
|
18 |
沈守娟, 郑广浩, 彭译萱, 等 基于YOLOv3算法的教室学生检测与人数统计方法[J]. 软件导刊, 2020, 19 (9): 78- 83 SHEN Shoujuan, PENG Yixuan, ZHENG Guanghao, et al Crowd detection and statistical methods based on YOLOv3 algorithm in classroom scenes[J]. Software Guide, 2020, 19 (9): 78- 83
|
19 |
DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16×16 words: Transformers for image recognition at scale [EB/OL]. (2020−06−03)[2023−06−22]. https://arxiv.org/pdf/2010.11929.
|
20 |
YU W, LUO M, ZHOU P, et al. MetaFormer is actually what you need for vision [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . New Orleans: IEEE, 2022: 10819−10829.
|
21 |
LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Honolulu: IEEE, 2017: 2117−2125.
|
22 |
SELVARAJU R R, COGSWELL M, DAS A, et al. Grad-CAM: visual explanations from deep networks via gradient-based localization [C]// Proceedings of the IEEE International Conference Computer Vision . Venice: IEEE, 2017.
|
23 |
LI Y, HU J, WEN Y, et al. Rethinking vision transformers for MobileNet size and speed [C]// Proceedings of the IEEE/CVF International Conference on Computer Vision . Paris: IEEE, 2023: 16889−16900.
|
24 |
邵明月, 张建华, 冯全, 等 深度学习在植物叶部病害检测与识别的研究进展[J]. 智慧农业(中英文), 2022, 4 (1): 29- 46 SHAO Mingyue, ZHANG Jianhua, FENG Quan, et al Research progress of deep learning in detection and recognition of plant leaf diseases[J]. Smart Agriculture, 2022, 4 (1): 29- 46
|
25 |
REDMON J, FARHADI A. YOLOv3: an incremental improvement [EB/OL]. (2018−04−08)[2023−06−22]. https://arxiv.org/pdf/1804.02767.
|
26 |
WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: trainable bag-of-freebies set new state-of-the-art for real-time object detectors [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Patterns Recognized . Vancouver: IEEE, 2023: 7464−7475.
|
27 |
GE Z, LIU S, WANG F, et al. YOLOX: exceeding YOLO series in 2021 [EB/OL]. (2021−08−06)[2023−06−22]. https://arxiv.org/pdf/2107.08430
|
28 |
CARION N, MASSA F, SYNNAEVE G, et al. End-to-end object detection with transformers [C]// Proceedings of European Conference on Computer Vision . Glasgow: Springer, 2020: 213−229.
|
29 |
TAN M, PANG R, LE Q V. EfficientDet: scalable and efficient object detection [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Seattle: IEEE, 2020: 10781−10790.
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|