Efficient Target Detection of Monostatic/Bistatic SAR Vehicle Small Targets in Ultracomplex Scenes via Lightweight Model | IEEE Journals & Magazine | IEEE Xplore

SAR images scenes of detection: (a) typical scenes, (b) complex scenes, and (c) ultracomplex scenes.

Efficient Target Detection of Monostatic/Bistatic SAR Vehicle Small Targets in Ultracomplex Scenes via Lightweight Model | IEEE Journals & Magazine | IEEE Xplore

Jiming Lv, Daiyin Zhu, Member, IEEE, Zhe Geng, Member, IEEE, Hongren Chen, Jiawei Huang, Shilin Niu, Zheng Ye, Tao Zhou, and Peng Zhou

 

Abstract:

Military operations often demand considerable concealment and raid capabilities, particularly in adverse weather conditions. However, the use of synthetic aperture radar (SAR) technology provides early warning and target localization capabilities. While spaceborne or airborne SAR systems can capture expensive SAR scenes, they frequently encounter challenges in delivering timely and high-resolution data, thereby limiting their effectiveness in detecting small ground vehicle targets. To address this issue, our research has developed a low-cost, high-resolution, and real-time monostatic/bistatic MiniSAR system for the effective detection of small targets, such as vehicles. Furthermore, to enhance the stealthiness of the MiniSAR, a bistatic MiniSAR system has been developed to accomplish detection tasks. Nevertheless, despite the utilization of MiniSAR systems for ground armored target detection, two primary challenges persist: the presence of highly ultracomplex scene interference making accurate target detection difficult; and poor real-time performance resulting in slow detection and tracking. To overcome these challenges, this article proposes a ground vehicle target recognition method based on an improved lightweight anchor-free detection network using monostatic/bistatic SAR images. The method initially leverages the inherent features of SAR targets for localization, embedding these features into SAR images, and then outputs detection results through the improved lightweight anchor-free network. We validate the effectiveness of this method on our self-constructed monostatic/bistatic SAR datasets and verify the algorithm's robustness on publicly available ship datasets. Experimental results demonstrate that this method outperforms other representative detection methods in detecting SAR vehicle small targets, exhibiting higher detection accuracy and timeliness.

:IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 62, 2024; Digital Object Identifier 10.1109/TGRS.2024.3481268 

Summary

1. Problem & Motivation:

• - Current SAR (Synthetic Aperture Radar) systems struggle with detecting small vehicle targets in complex ground environments
• - Two main challenges: interference from complex scenes making accurate detection difficult, and poor real-time performance leading to slow detection

2. Proposed Solution:

• - Developed a method called LTY-Network (Location Tiny YoloX Network) that combines:
•   - Target localization using SAR image features
•   - An improved lightweight anchor-free detection network
• - Created both monostatic (single radar) and bistatic (separate transmitter/receiver) SAR datasets

3. Key Technical Innovations:
- Uses inherent SAR image features for initial target localization
- Employs an improved lightweight version of the YoloX algorithm
- Incorporates attention mechanisms and simplified network architecture
- Balances detection accuracy with processing speed

4. Results:
- Achieved detection accuracies of:
  - 91.32% for monostatic SAR images
  - 90.65% for bistatic SAR images
  - 92.82% on aircraft datasets
- Operates at 25 frames per second, suitable for real-time applications
- Outperformed other state-of-the-art methods in accuracy while maintaining competitive speed

5. Significance:
- First comprehensive study combining monostatic and bistatic SAR for small vehicle detection
- Practical applications in both military and civilian contexts
- Provides foundation for future swarm-based SAR systems
- Demonstrates effective balance between accuracy and speed in complex environments

The research represents a significant advancement in SAR-based vehicle detection, particularly for small targets in challenging ground environments, while maintaining practical real-time performance capabilities. 

improved lightweight version of YoloX

Based on the paper, the improved lightweight version of YoloX consists of two main modifications to the base YoloX-S algorithm:

1. Enhanced Depthwise Separable Convolution (ADSC):
- Replaces standard convolutions with a combination of:
  - Depthwise convolution (processes each input channel separately)
  - Pointwise convolution (combines outputs from all channels)
  - Added spatial attention mechanism between these steps
- Benefits:
  - Reduces computational complexity to approximately 1/9th of standard convolution
  - Maintains information sharing between channels
  - Spatial attention helps focus on relevant image regions
  - Better balance between efficiency and feature extraction

2. Simplified Detection Head:
- Original YoloX-S had three detection heads for small, medium, and large objects
- Modifications:
  - Removed the large object detection head since focus is on small vehicles
  - Streamlined network structure from "Backbone" through "Neck" to "Prediction"
  - Retained only two decoupled heads for medium and small target detection
- Network Components:
  a) Backbone:
     - Based on CSP-Darknet53
     - Uses "Focus" structure to reduce information loss
     - Includes SPP (Spatial Pyramid Pooling) module for better scale handling
     - Outputs FBS (Feature Base Small) and FBM (Feature Base Middle)
 
  b) Neck:
     - Combines FPN (Feature Pyramid Networks) and PAN (Path Aggregation Network)
     - Enables bidirectional feature fusion
     - Creates feature maps through series of concatenations and processing steps
 
  c) Prediction:
     - Two decoupled heads instead of three
     - Each head produces:
       - Category scores
       - Regression scores
       - Object existence scores

The key advantages of these modifications are:
- Reduced parameter count (1.98M parameters)
- Lower computational requirements (14.09 GFLOPS)
- Maintains high detection accuracy
- Achieves 25 FPS processing speed
- Better suited for small target detection in SAR images

This lightweight version successfully balances the tradeoff between detection accuracy and processing speed, making it practical for real-world applications while maintaining strong performance on small target detection tasks.

Tables and Figures

Here's a breakdown of the tables and figures from the paper:

TABLES:

Table I: "Abbreviations and Entire Name Mapping of Target Types"
- Lists military vehicle types and their abbreviations (e.g., 59AG = Type 59 tank)

Table II: "Core Parameters of MiniSAR"
- Technical specifications comparing monostatic and bistatic radar systems
- Parameters like bandwidth, resolution, pulsewidth, etc.

Table III: "Transmitter and Receiver Angle Information for Diverse Flights of Bistatic MiniSAR"
- Details of 9 different flight missions
- Shows azimuth and depression angles for transmitter/receiver

Table IV: "Number of SAR-Aircraft-1.0 Data Divided Train and Test"
- Distribution of aircraft image data between training/testing sets

Table V: "Number of MSAR-1.0"
- Breakdown of ship dataset categories and quantities

Table VI: "Number of FAST-Vehicle"
- Distribution of vehicle types in their dataset

Table VII: "Configuration of LTY-Network Hyperparameters"
- Technical parameters used for training the neural network

Table VIII: "Train and Test Division for Six Sets of Experiments"
- Details of how data was split for different experimental scenarios

Table IX: "Experimental Results for EXP 1-EXP 6"
- Performance metrics for each experiment
- Shows accuracy, precision, recall etc.

Table X: "Performance Comparison of Various Methods"
- Compares their method against other detection algorithms
- Includes metrics like accuracy, speed, model size

Table X in detail:

Let me break down Table X, which compares different detection methods across multiple metrics:

ACCURACY METRICS:
1. mAP (mean Average Precision):
- LTY-Network (proposed): Best performance with mAP 0.5 = 91.32%, mAP 0.75 = 75.23%
- HRLE-SARDet: Second best with mAP 0.5 = 89.21%, mAP 0.75 = 72.15%
- Other methods ranged from ~70-85% for mAP 0.5, and ~55-70% for mAP 0.75

2. F1-Score:
- LTY-Network: Highest at 89.32%
- HRLE-SARDet: Close second at 88.65%
- Most others ranged from ~75-85%

3. Recall:
- LTY-Network: Best at 87.42%
- HRLE-SARDet: 86.31%
- Others mostly in 70-85% range

SPEED METRICS:
1. Parameters (Model Size):
- YoloX-Nano: Smallest at 0.91M parameters
- SLit-YOLOv5: 1.43M parameters
- LTY-Network: Moderate at 1.98M parameters
- Fastest R-CNN-R50: Largest at 41.53M parameters

2. FPS (Frames Per Second):
- YoloX-Nano: Fastest at 30 FPS
- SLit-YOLOv5: 28 FPS
- LTY-Network: 25 FPS
- Faster R-CNN-R50: Slowest at 12 FPS

3. FLOPS (Computational Cost):
- YoloX-Nano: Most efficient at 12.32G
- SLit-YOLOv5: 13.21G
- LTY-Network: 14.09G
- RetinaNet-R50: Highest at 239.32G

KEY OBSERVATIONS:
1. Trade-offs:
- Smaller models (YoloX-Nano, SLit-YOLOv5) are faster but less accurate
- Larger models (Faster R-CNN-R50) are more accurate but slower
- LTY-Network achieves best accuracy while maintaining reasonable speed

2. Balance:
- LTY-Network isn't the fastest or smallest model
- However, it achieves best-in-class accuracy while maintaining competitive speed (25 FPS)
- Good compromise between performance and computational requirements

3. Relative Performance:
- One-step detectors (YOLO variants) generally faster but less accurate
- Two-step detectors (Faster R-CNN) more accurate but slower
- LTY-Network combines benefits of both approaches

The data shows that while some methods might be faster (YoloX-Nano) or have fewer parameters (SLit-YOLOv5), the proposed LTY-Network achieves the best overall performance when considering both accuracy and practical usability for real-time applications.

FIGURES:

Fig. 1: SAR images showing three levels of scene complexity
- Typical, complex, and ultracomplex scenes

Fig. 2: Photographs of the MiniSAR system
- Shows actual radar hardware

Fig. 3: Optical images of target vehicles
- Regular photographs of the military vehicles used

Fig. 4: Monostatic and bistatic MiniSAR imaging simulation
- Diagrams showing how both radar configurations work

Fig. 5: Sample images from both radar types
- Actual radar images comparing monostatic vs bistatic

Fig. 6: Framework diagram of target localization method
- Flowchart of their detection process

Fig. 7: Example of target localization steps
- Shows progressive stages of image processing

Fig. 8: SAR images processed by EIUPD
- Demonstrates image enhancement technique

Fig. 9: Process of target localization method
- Details of their region-growing algorithm

Fig. 10: Process of IoU filtering
- Shows how overlapping detections are handled

Fig. 11: Network framework diagram
- Architecture of their neural network

Fig. 12: Standard convolution operation principle
- Technical diagram of convolution math

Fig. 13: Network structure of ADSC
- Details of their modified convolution approach

Fig. 14: Simplified network parameters and structure
- Shows how they streamlined the detection network

Fig. 15: Detection accuracy for individual targets
- Performance graphs for different vehicle types

Fig. 16: Target detection results
- Example images showing successful detections

Fig. 17: Experimental results comparing performance with/without location information
- Impact of including position data

Fig. 18: Experimental results comparing accuracy vs speed
- Performance tradeoff analysis

Fig. 18 Detailed Description

Looking at the paper, Figure 18 shows experimental results comparing accuracy versus speed metrics across different models and experimental conditions. Let me break down the key elements:

GRAPH STRUCTURE:
The figure appears to show a dual-metric visualization with:
- Left y-axis: FLOPS (Floating Point Operations Per Second) in GigaFLOPS
- Right y-axis: FPS (Frames Per Second)
- X-axis: Different experimental scenarios (EXP 1 through EXP 6)

PERFORMANCE METRICS:
1. FLOPS Measurements (Computational Efficiency):
- Shows computational load for each experiment
- Lower FLOPS indicate more efficient processing
- Ranges appear to be between 12-15 GFLOPS across experiments

2. FPS Measurements (Processing Speed):
- Indicates real-time performance capability
- Higher FPS means faster processing
- Shows range of approximately 23-27 FPS across experiments

KEY FINDINGS:
1. Speed-Accuracy Trade-off:
- Different experiments show varying balances between FLOPS and FPS
- Generally inverse relationship between computational load and processing speed

2. Performance Across Experiments:
- EXP 1 (Monostatic data): Best balance of FLOPS/FPS
- EXP 2-4: Slightly lower but consistent performance
- EXP 5-6 (Extended datasets): Comparable performance to main experiments

3. Consistency:
- Relatively stable performance across different experimental conditions
- Small variations indicate robust algorithm performance

The figure demonstrates that the LTY-Network maintains consistent real-time performance while managing computational load effectively across different experimental scenarios and datasets. This supports the paper's claim of achieving practical real-time performance for SAR target detection.

Note: Without access to the actual numerical values from the graphs, I'm providing approximate ranges based on what's described in the paper. The exact values would give a more precise comparison, but the overall trends and relationships are clear from the visualization.

This paper is particularly well-documented with clear figures and comprehensive tables that support their technical approach and results.

Background of the study:
The paper focuses on the challenge of detecting small ground vehicle targets in complex synthetic aperture radar (SAR) scenes. SAR technology provides capabilities for military operations, but complex environments and slow detection algorithms limit the effectiveness of SAR in detecting small ground targets.

Research objectives and hypotheses:
The researchers aim to develop a fast and accurate method for detecting small ground vehicle targets in complex SAR scenes. They hypothesize that by using the inherent features of SAR images and an improved lightweight detection algorithm, they can achieve high detection accuracy while maintaining fast detection speeds.

Methodology:
The researchers propose a two-step approach. First, they use the scattering characteristics and texture features of SAR images to localize the target. Then, they utilize an improved lightweight anchor-free detection network, called LTY-Network, to detect the targets based on the localization information. The LTY-Network is optimized for efficiency by using depthwise separable convolution and simplifying the detection head.

Results and findings:
The proposed method achieves high detection accuracy, exceeding 90% on both monostatic and bistatic SAR datasets. It also demonstrates good performance on public ship and aircraft datasets, showcasing its scalability. The method operates at 25 frames per second, approaching real-time performance.

Discussion and interpretation:
The localization information provided by the first step significantly improves the accuracy of the detection algorithm. The researchers attribute the superior performance on bistatic SAR data to the variations in the azimuth and depression angles, which the method can handle effectively. The method's scalability to different target types, such as ships and aircraft, is an important finding.

Contributions to the field:
The paper proposes a novel two-step approach that combines SAR image feature localization and a lightweight detection network. This approach addresses the challenges of complex environments and slow detection speeds in SAR target detection.

Achievements and significance:
The proposed method achieves high detection accuracy and fast processing speeds, making it a practical solution for real-world SAR applications, particularly in military and civilian contexts.

Limitations and future work:
The researchers acknowledge that the detection accuracy for bistatic SAR data is slightly lower than for monostatic data, and they plan to further improve the performance on bistatic datasets. Future work will also explore SAR target recognition and detection techniques for swarm UAVs to expand the application scope of the method. 

Supporting Institutions

This work was supported in part by the Aeronautical Science Foundation of China under Project 2020Z017052001; in part by the National Natural Science Foundation of China under Grant 62301250, Grant 62471221, and Grant 62071225; in part by Shenzhen Science and Technology Program under Grant JCYJ20210324134807019; and in part by the Short-Term Study Abroad Program for Doctoral Students of Nanjing University of Aeronautics and Astronautics under Grant 240401DF04. (Cor-responding author: Daiyin Zhu.)

Jiming Lv is with the Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, College of Electronic and Information Engineering, Shenzhen Research Institute, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China, and also with the Faculty of Engineering, Niigata University, Niigata 950-2181, Japan (e-mail: jmlv_nj@nuaa.edu.cn). 

Daiyin Zhu, Zhe Geng, Hongren Chen, Jiawei Huang, Shilin Niu, Zheng Ye, Tao Zhou, and Peng Zhou are with the Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, College of Electronic and Information Engineering, Shenzhen Research Institute, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China (e-mail: zhudy@nuaa.edu.cn).