ATT Squeeze U-Net: A Lightweight Network for Forest Fire Detection and Recognition
Forest fire is becoming one of the most significant natural disasters at the expense of ecology and economy. In this article, we develop an effective SqueezeNet based asymmetric encoder-decoder U-shape architecture, Attention U-Net and SqueezeNet (ATT Squeeze U-Net), mainly functions as an extractor...
Main Authors: | Jianmei Zhang, Hongqing Zhu, Pengyu Wang, Xiaofeng Ling |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9319207/ |
Similar Items
-
SqueezeNet: An Improved Lightweight Neural Network for Sheep Facial Recognition
by: Min Hao, et al.
Published: (2024-02-01) -
Real-Time Vehicle Make and Model Recognition with the Residual SqueezeNet Architecture
by: Hyo Jong Lee, et al.
Published: (2019-02-01) -
Weight-Quantized SqueezeNet for Resource-Constrained Robot Vacuums for Indoor Obstacle Classification
by: Qian Huang
Published: (2022-03-01) -
SqueezeNet and Fusion Network-Based Accurate Fast Fully Convolutional Network for Hand Detection and Gesture Recognition
by: Baohua Qiang, et al.
Published: (2021-01-01) -
LeanNet: An Efficient Convolutional Neural Network for Digital Number Recognition in Industrial Products
by: Na Qin, et al.
Published: (2021-05-01)