AMFLW-YOLO: A Lightweight Network for Remote Sensing Image Detection Based on Attention Mechanism and Multiscale Feature Fusion

The scale of targets in remote sensing images varies greatly and is diverse. It has many small targets that are distributed densely and high complexity of image background. The number of network model parameters and the computation amount of the object detection algorithms based on deep learning is...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2023, Vol.61, p.1-16
Main Authors: Peng, Guili, Yang, Zijian, Wang, Shoubin, Zhou, Yuan
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Attention mechanism
Computation
Computer networks
Convolution
Convolutional neural networks
Deep learning
Detection
Feature extraction
feature fusion
Image detection
Lightweight
lightweight network
Machine learning
Mathematical models
Model accuracy
Object detection
Object recognition
Optical sensors
Parameters
Remote sensing
remote sensing image
Sensors
Task analysis
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Summary:The scale of targets in remote sensing images varies greatly and is diverse. It has many small targets that are distributed densely and high complexity of image background. The number of network model parameters and the computation amount of the object detection algorithms based on deep learning is huge. It is difficult to apply them on a platform with fixed performance and limited computing resources. A lightweight remote sensing object detection model is proposed in this article, which called attention and multiscale feature fusion lightweight-YOLO (AMFLW-YOLO). The deep separable convolution, inverted residual, and linear bottleneck structure are employed to replace the standard convolution layer to reduce the model parameters in the backbone network of the model. The coordinate attention (CA) mechanism is introduced into the feature fusion network to capture the direction- and location-aware information across channels at the same time, which improves the accuracy of the network. The bidirectional feature pyramid network (BiFPN) structure is employed to strengthen feature extraction. The learnable weights are introduced to learn the importance of different input features. The multiscale feature fusion is applied to improve the detection effect. The experimental results show that the algorithm achieves satisfactory performance in terms of efficiency and accuracy and has advantages in detection accuracy and model lightweight.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2023.3327285