Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (11): 3580-3587.DOI: 10.11772/j.issn.1001-9081.2021122164
• ChinaVR 2021 • Previous Articles
Haiyan SUN, Yunbo CHEN, Dingwei FENG, Tong WANG, Xingquan CAI()
Received:
2021-12-24
Revised:
2022-03-14
Accepted:
2022-03-17
Online:
2022-05-17
Published:
2022-11-10
Contact:
Xingquan CAI
About author:
SUN Haiyan, born in 1980, Ph. D., lecturer. Her research interests include virtual reality, deep learning.Supported by:
通讯作者:
蔡兴泉
作者简介:
孙海燕(1980—),女,山东济宁人,讲师,博士,主要研究方向:虚拟现实、深度学习基金资助:
CLC Number:
Haiyan SUN, Yunbo CHEN, Dingwei FENG, Tong WANG, Xingquan CAI. Forest pest detection method based on attention model and lightweight YOLOv4[J]. Journal of Computer Applications, 2022, 42(11): 3580-3587.
孙海燕, 陈云博, 封丁惟, 王通, 蔡兴泉. 基于注意力模型和轻量化YOLOv4的林业害虫检测方法[J]. 《计算机应用》唯一官方网站, 2022, 42(11): 3580-3587.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021122164
害虫种类 | 样本数量 | 害虫种类 | 样本数量 |
---|---|---|---|
Boerner | 2 232 | armandi | 2 346 |
Leconte | 2 450 | coleoptera | 2 091 |
Linnaeus | 1 860 | linnaeus | 1 967 |
acuminatus | 1 604 |
Tab. 1 Number statistics of samples of different species of pests
害虫种类 | 样本数量 | 害虫种类 | 样本数量 |
---|---|---|---|
Boerner | 2 232 | armandi | 2 346 |
Leconte | 2 450 | coleoptera | 2 091 |
Linnaeus | 1 860 | linnaeus | 1 967 |
acuminatus | 1 604 |
模型 | AP/% | mAP/% | 帧率/FPS | ||||||
---|---|---|---|---|---|---|---|---|---|
Boerner | Leconte | Linnaeus | acuminatus | armandi | coleoptera | linnaeus | |||
YOLOv4 | 95.9 | 94.8 | 85.6 | 70.5 | 89.2 | 81.9 | 91.5 | 87.0 | 25 |
本文模型 | 99.7 | 98.6 | 90.1 | 84.0 | 95.0 | 91.1 | 97.5 | 93.7 | 56 |
Tab. 2 Comparison of objective data of proposed model and original YOLOv4 model
模型 | AP/% | mAP/% | 帧率/FPS | ||||||
---|---|---|---|---|---|---|---|---|---|
Boerner | Leconte | Linnaeus | acuminatus | armandi | coleoptera | linnaeus | |||
YOLOv4 | 95.9 | 94.8 | 85.6 | 70.5 | 89.2 | 81.9 | 91.5 | 87.0 | 25 |
本文模型 | 99.7 | 98.6 | 90.1 | 84.0 | 95.0 | 91.1 | 97.5 | 93.7 | 56 |
MobileNetV3 | 轻量化 PANet | +CBAM | +Focal Loss | mAP/% | 帧率/FPS |
---|---|---|---|---|---|
87.0 | 25 | ||||
| 86.5 | 43 | |||
| 87.8 | 34 | |||
| 90.2 | 26 | |||
| 91.0 | 30 | |||
| | | | 93.7 | 56 |
Tab. 3 Results of ablation experiments
MobileNetV3 | 轻量化 PANet | +CBAM | +Focal Loss | mAP/% | 帧率/FPS |
---|---|---|---|---|---|
87.0 | 25 | ||||
| 86.5 | 43 | |||
| 87.8 | 34 | |||
| 90.2 | 26 | |||
| 91.0 | 30 | |||
| | | | 93.7 | 56 |
注意力模块 | mAP/% | 帧率/FPS |
---|---|---|
无 | 87.0 | 25 |
+SE | 87.8 | 25 |
+ECA | 89.4 | 26 |
+CBAM | 90.2 | 26 |
Tab. 4 Comparison of different attention mechanisms
注意力模块 | mAP/% | 帧率/FPS |
---|---|---|
无 | 87.0 | 25 |
+SE | 87.8 | 25 |
+ECA | 89.4 | 26 |
+CBAM | 90.2 | 26 |
模型 | mAP/% | 帧率/FPS | 模型 | mAP/% | 帧率/FPS |
---|---|---|---|---|---|
Faster‑RCNN | 86.6 | 15 | 文献[ | 84.5 | 50 |
SSD | 79.8 | 23 | 本文模型 | 93.7 | 56 |
YOLOv5 | 91.6 | 36 |
Tab. 5 Comparison of proposed model and other models
模型 | mAP/% | 帧率/FPS | 模型 | mAP/% | 帧率/FPS |
---|---|---|---|---|---|
Faster‑RCNN | 86.6 | 15 | 文献[ | 84.5 | 50 |
SSD | 79.8 | 23 | 本文模型 | 93.7 | 56 |
YOLOv5 | 91.6 | 36 |
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