Infrared image target detection for substation electrical equipment based on improved faster region-based convolutional neural network algorithm

规范化(社会学) 计算机科学 卷积神经网络 人工智能 算法 残余物 模式识别(心理学) 激活函数 特征提取 块(置换群论) 人工神经网络 数学 几何学 社会学 人类学
作者
Changdong Wu,Yanliang Wu,Xu He
出处
期刊:Review of Scientific Instruments [American Institute of Physics]
卷期号:95 (4) 被引量:2
标识
DOI:10.1063/5.0200826
摘要

Substation electrical equipment generates a massive number of infrared images during operation. However, the overall quality of the infrared images is low and it lacks image detail information. When using traditional target detection algorithms for detection, feature extraction poses great difficulties. Therefore, to address this problem, this paper proposes a target detection algorithm based on the improved faster region-based convolutional neural network (Faster R-CNN). It achieves the correct identification of different types of electrical equipment in infrared images. First, the algorithm improves the backbone network of Faster R-CNN for feature extraction. An InResNet structure is proposed to replace the residual block structure of the original ResNet-34 network, which enhances the richness of feature extraction. Second, the rectified linear unit activation function in the original feature extraction network is replaced by the exponential linear unit activation function, and group normalization is used instead of batch normalization as the network normalization method. Then, the dense connection structure is introduced into the ResNet-34 network, and the whole network is called residual dense connection network. Finally, the improved Faster R-CNN is compared to the original Faster R-CNN, a single-shot multibox detector, and you only look once v3 plus spatial pyramid pooling. The experimental results show that the improved algorithm has the highest mean average precision and average recall for most of the substation electrical equipment in infrared images. Moreover, from the confidence level of the detected electrical equipment and the accuracy of the prediction box, the improved Faster R-CNN has the best performance.
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
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
岳努努关注了科研通微信公众号
刚刚
LinJunhong发布了新的文献求助10
1秒前
完美世界应助jiangshanshan采纳,获得10
1秒前
1秒前
1秒前
Crema发布了新的文献求助10
1秒前
2秒前
dudu发布了新的文献求助10
2秒前
王一一发布了新的文献求助10
2秒前
FOODHUA完成签到,获得积分10
3秒前
量子星尘发布了新的文献求助10
3秒前
4秒前
LinJunhong完成签到,获得积分10
5秒前
哈哈完成签到,获得积分10
6秒前
npp完成签到,获得积分10
6秒前
levy发布了新的文献求助30
6秒前
36456657应助charryyu采纳,获得30
6秒前
7秒前
7秒前
紫愿发布了新的文献求助10
7秒前
苏苏完成签到 ,获得积分10
8秒前
8秒前
Crema完成签到,获得积分10
8秒前
9秒前
ziyiziyi发布了新的文献求助10
10秒前
隐形曼青应助jingdaitianxiang采纳,获得10
10秒前
量子星尘发布了新的文献求助10
11秒前
何1完成签到,获得积分10
11秒前
11秒前
hx关注了科研通微信公众号
11秒前
11秒前
wanci应助trayheep采纳,获得10
11秒前
12秒前
慕容松完成签到,获得积分10
12秒前
彭于晏应助哈哈采纳,获得10
12秒前
12秒前
12秒前
12秒前
酷波er应助乐观的莫茗采纳,获得10
13秒前
如初完成签到,获得积分10
13秒前
高分求助中
Production Logging: Theoretical and Interpretive Elements 2700
Neuromuscular and Electrodiagnostic Medicine Board Review 1000
Statistical Methods for the Social Sciences, Global Edition, 6th edition 600
こんなに痛いのにどうして「なんでもない」と医者にいわれてしまうのでしょうか 510
The Insulin Resistance Epidemic: Uncovering the Root Cause of Chronic Disease  500
Walter Gilbert: Selected Works 500
An Annotated Checklist of Dinosaur Species by Continent 500
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 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3662961
求助须知:如何正确求助?哪些是违规求助? 3223721
关于积分的说明 9752858
捐赠科研通 2933645
什么是DOI,文献DOI怎么找? 1606229
邀请新用户注册赠送积分活动 758325
科研通“疑难数据库(出版商)”最低求助积分说明 734785