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license: apache-2.0
tags:
- salient-object-detection
- remote-sensing
- computer-vision
- pytorch
---
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<h1>🚀 AESINet: Adaptive Edge-aware Semantic Interaction Network for Salient Object Detection in Optical Remote Sensing Images</h1>
<p>
<b>Xiangyu Zeng</b><sup>1</sup>
<b>Mingzhu Xu</b><sup>1</sup>
<b>Yijun Hu</b><sup>1</sup>
<b>Haoyu Tang</b><sup>1</sup>
<b>Yupeng Hu</b><sup>1✉</sup>
<b>Liqiang Nie</b><sup>1</sup>
</p>
<p>
<sup>1</sup>Affiliation (Please update if needed)
</p>
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Official implementation of **AESINet**, an Adaptive Edge-aware Semantic Interaction Network for Salient Object Detection (SOD) in Optical Remote Sensing Images.
🔗 **Journal:** IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2023
🔗 **Task:** Salient Object Detection (SOD)
🔗 **Framework:** PyTorch
---
## 📌 Model Information
### 1. Model Name
**AESINet** (Adaptive Edge-aware Semantic Interaction Network)
---
### 2. Task Type & Applicable Tasks
- **Task Type:** Salient Object Detection / Remote Sensing
- **Core Task:** Salient object detection in optical remote sensing imagery
- **Applicable Scenarios:**
- Remote sensing scene understanding
- Aerial image analysis
- Environmental monitoring
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### 3. Project Introduction
Salient Object Detection (SOD) in optical remote sensing images is challenging due to complex backgrounds, low contrast, and ambiguous object boundaries.
**AESINet** introduces an **adaptive edge-aware semantic interaction mechanism**, which:
- Enhances edge-aware feature representation
- Promotes multi-level semantic interaction
- Improves boundary localization accuracy
- Strengthens robustness under complex backgrounds
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### 4. Training Data
Common datasets used in remote sensing SOD:
- ORSSD
- EORSSD
- ORSI
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## 🚀 Pre-trained Weights
### Model Weights
- **AESINet-V (VGG backbone):**
https://pan.baidu.com/s/1Xo97lQF4TS2jak9v8iU8jA?pwd=qegm
- **AESINet-R (ResNet backbone):**
https://pan.baidu.com/s/1gYW9qOjR0YjU5R4dCN9Hfg?pwd=tj25
---
### Backbone Pretrained Weights
- **VGG & ResNet:**
https://pan.baidu.com/s/18k9e3YcxK1rTY8A_WajTyg?pwd=lb8l
---
## 🚀 Training & Testing
### Step 1: Prepare Data
- Download datasets and pre-trained weights
- Place them into corresponding directories
### Step 2: Generate Dataset Lists
```bash
python generateTrainList.py
python generateTestList.py
### Step 3: Configure Paths
Modify dataset paths in the code
Ensure .txt file paths are correctly set
## ⚠️ Notes
Both ResNet and VGG versions are available
Code readability will be improved in future updates
GPU is recommended for training and inference
## 📝 Citation
@ARTICLE{10198281,
author={Zeng, Xiangyu and Xu, Mingzhu and Hu, Yijun and Tang, Haoyu and Hu, Yupeng and Nie, Liqiang},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={Adaptive Edge-Aware Semantic Interaction Network for Salient Object Detection in Optical Remote Sensing Images},
year={2023},
volume={61},
pages={1-16},
doi={10.1109/TGRS.2023.3300317}
}
## 📬 Contact
For any questions, please contact:
## 📧 z15264367990@163.com |