Pest-YOLO: Deep Image Mining and Multi-Feature Fusion for Real-Time Agriculture Pest Detection

计算机科学 卷积神经网络 有害生物分析 人工智能 特征(语言学) 特征提取 目标检测 模式识别(心理学) 数据挖掘 语言学 哲学 业务 营销
作者
Zhe Tang,Zhengyun Chen,Fang Qi,Lingyan Zhang,Shuhong Chen
标识
DOI:10.1109/icdm51629.2021.00169
摘要

The frequent outbreaks of agriculture pests have caused heavy losses in crop production. And the small size and high similarity of agricultural pests bring challenges to the prompt and accurate pest detection using imaging technologies. The key impetus of this paper is to achieve a good balance between efficiency and accuracy for pest detection on the basis of agricultural image data mining. This paper proposes Pest-YOLO which is a real-time agriculture pest detection method based on the improved convolutional neural network (CNN) and YOLOv4. First, a squeeze-and-excitation attention mechanism module is introduced to CNN for mining image data, extracting key features, and suppressing unrelated features. Then, a cross-stage multi-feature fusion method is designed to improve the structure of feature pyramid network and path aggregation network, thus enhancing the feature expressiveness of small targets like pests. Finally, our Pest-YOLO realizes end-to-end real-time pest detection with high accuracy based on improved CNN and YOLOv4. We evaluate the performance of our method on a typical large-scale pest dataset including 28k images and 24 classes. Experimental results demonstrate that our method outperforms the state-of-the-art solutions including Faster R-CNN and YOLO-based detectors, and achieves good performance with 71.6% mAP and 83.5% Recall. The proposed method is effective and applicable for accurate and real-time intelligent pest detection without expertise feature engineering.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
2秒前
kkkkk完成签到,获得积分10
2秒前
三斤完成签到 ,获得积分20
2秒前
李茵发布了新的文献求助10
3秒前
3秒前
4秒前
4秒前
wyl发布了新的文献求助10
5秒前
6秒前
6秒前
小小小值钱完成签到,获得积分20
8秒前
wangjue发布了新的文献求助10
9秒前
10秒前
10秒前
木可发布了新的文献求助10
11秒前
11秒前
wyl完成签到,获得积分10
13秒前
汉堡包应助三斤采纳,获得10
14秒前
wangqiuhong发布了新的文献求助10
16秒前
17秒前
失眠的夜梦关注了科研通微信公众号
17秒前
今后应助HIT_C采纳,获得10
18秒前
今后应助SuperZzz采纳,获得10
19秒前
ZONG发布了新的文献求助10
21秒前
Nugget发布了新的文献求助10
22秒前
李茵完成签到,获得积分10
24秒前
量子星尘发布了新的文献求助10
24秒前
汉堡包应助风趣的老太采纳,获得10
25秒前
DongWei95发布了新的文献求助30
27秒前
27秒前
27秒前
猪猪hero发布了新的文献求助10
27秒前
斯文念波发布了新的文献求助10
27秒前
29秒前
31秒前
31秒前
断数循环应助任峰采纳,获得10
32秒前
FIN应助wuyu采纳,获得30
36秒前
越野蟹完成签到 ,获得积分10
36秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989406
求助须知:如何正确求助?哪些是违规求助? 3531522
关于积分的说明 11254187
捐赠科研通 3270174
什么是DOI,文献DOI怎么找? 1804901
邀请新用户注册赠送积分活动 882105
科研通“疑难数据库(出版商)”最低求助积分说明 809174