Image Recognition Based on the Depth-Wise Separable Convolution and Softpool

联营 卷积(计算机科学) 计算机科学 特征(语言学) 人工智能 模式识别(心理学) 图像(数学) 特征提取 可分离空间 卷积神经网络 垃圾 数据挖掘 计算机视觉 人工神经网络 数学 数学分析 哲学 语言学 程序设计语言
作者
Linlin Wang,Xiaoyu Fang,Tao Hong,Chang Liu,Shilan Liu
标识
DOI:10.1109/prai55851.2022.9904247
摘要

For the purpose of enabling the garbage classification to work accurately and efficiently, the image recognition method based on improved Inception-ResNet-V2 network is studied, and four types of daily domestic wastes are classified and identified. In the proposed network, the connection structure in the primary inception module is improved to achieve a dense connection, Softpool is applied to replace the traditional Maxpool pooling method, fine-grained feature information is retained, more intensive feature activations are enlarged, and the Depth-wise separable convolution is used to replace the common convolution method. The improved network not only reduces the quantity of calculation and expedites the training speed for the network, but also captures more image features fully, thereby the recognition accuracy is improved further. Compared with the ResNet50, AlexNet, and YOLOv5 network model, the results show that the recognition accuracy of the network model proposed in this paper comes up to 96.8%, which is 5% higher than that of the YOLOv5 network. The performance of the improved network is significantly enhanced comparing with the traditional network. It is proved that the algorithm is eligible to be successfully applied to the problem of garbage classification, and it greatly weakens the difficulty of municipal garbage recovery.
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
刚刚
坦率的正豪完成签到,获得积分10
刚刚
酷波er应助甜美不评采纳,获得10
1秒前
2秒前
2秒前
木木发布了新的文献求助10
3秒前
Emily发布了新的文献求助10
3秒前
共行完成签到 ,获得积分10
3秒前
3秒前
顾矜应助科研通管家采纳,获得10
4秒前
FashionBoy应助科研通管家采纳,获得10
4秒前
ll应助科研通管家采纳,获得10
4秒前
科研通AI5应助科研通管家采纳,获得10
4秒前
ding应助科研通管家采纳,获得10
4秒前
汉堡包应助科研通管家采纳,获得10
4秒前
ll应助科研通管家采纳,获得10
5秒前
ll应助科研通管家采纳,获得10
5秒前
充电宝应助科研通管家采纳,获得10
5秒前
大模型应助科研通管家采纳,获得10
5秒前
星辰大海应助科研通管家采纳,获得10
5秒前
5秒前
5秒前
5秒前
6秒前
15858833895发布了新的文献求助10
7秒前
7秒前
在水一方应助影子采纳,获得10
7秒前
默默问晴完成签到,获得积分10
7秒前
zly发布了新的文献求助10
8秒前
8秒前
JamesPei应助忧虑的代芙采纳,获得10
8秒前
11秒前
wtt发布了新的文献求助10
13秒前
13秒前
南宫书瑶发布了新的文献求助10
14秒前
高跟鞋陈煋完成签到,获得积分10
15秒前
甜美不评发布了新的文献求助10
16秒前
搜集达人应助zly采纳,获得10
16秒前
Rollin完成签到 ,获得积分10
16秒前
17秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
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 3975610
求助须知:如何正确求助?哪些是违规求助? 3519986
关于积分的说明 11200337
捐赠科研通 3256337
什么是DOI,文献DOI怎么找? 1798246
邀请新用户注册赠送积分活动 877446
科研通“疑难数据库(出版商)”最低求助积分说明 806357