.png){kind=icon}
返回
Garbage Classification Algorithm Based on Improved MobileNetV3
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2024.3381533.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In recent years, the amount of household waste has increased sharply, and there is an urgent need to use intelligent waste classification equipment to assist in completing waste classification tasks. However, existing garbage classification algorithms have large parameter counts and poor real-time performance, which are not suitable for embedded garbage classification devices. Therefore, in order to achieve a lightweight and efficient classification model, this article uses a self-built garbage dataset and a pre-trained MobileNetV3 model in the PyTorch framework for garbage recognition and classification. We introduce the CBAM attention mechanism in the network model to enhance spatial feature perception. Utilize the Mish activation function to fully utilize the extracted depth image information. Using global average pooling instead of the fully connected layer of the original model reduces the number of model parameters while improving the recognition accuracy of the model. Finally, we propose an improved lightweight model called GMC-MobileNetV3. The experimental results show that the recognition accuracy of the improved MobileNetV3 model on self-built data set reaches 96.55%, which is 3.6% higher than that of the original model, the number of model parameters is 0.64M, the memory resource consumption is reduced by 56.6%, and the recognition time of a single garbage image is only 26.4ms. The network proposed in this article can achieve low consumption and high accuracy in garbage recognition and classification, providing reference for future academic research and engineering practice.
Keyword:
Convolution
Data models
Training
Convolutional neural networks
Sorting
Standards
Kernel
Classification algorithms
convolutional networks
deep learning
image recognition
transfer learning
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
The Arabidopsis BODENLOS gene encodes an auxin response protein inhibiting MONOPTEROS-mediated embryo patterning 拟南芥BODENLOS 基因编码抑制MONOPTEROS介导的胚胎模式的生长素反应蛋白
OAAI
OAAI分享收藏分享收藏A Novel Multi-Branch Channel Expansion Network for Garbage Image Classification一种新型的垃圾图像分类多分支通道扩展网络.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容