How MLtwist Streamlined AI Data Processing for SAR-Based Ship Detection Models

The Use Case

A leading Defense company set out to improve vessel detection models by leveraging Synthetic Aperture Radar (SAR) imagery. Training these models required annotating thousands of SAR images with tilted bounding boxes that aligned precisely with each vessel’s contours—sometimes labeling over 100 ships in a single image.

The raw SAR data was delivered as large tiled images, which had to be split into smaller, more manageable images for annotation. Once labeled, the data needed to be packaged and delivered in the customer’s exact preferred format to integrate seamlessly into their AI model pipeline.

THE CHALLENGE

High-Density, Precision Annotation on SAR Imagery

The project posed several unique challenges:

• Time-Intensive Labeling: High vessel density in each SAR image meant traditional annotation methods were too slow.
• Tilted Bounding Box Accuracy: To maximize detection performance, bounding boxes needed to match each vessel’s orientation, not just use standard rectangular boxes.
• Complex Preprocessing of SAR Tiles: The original SAR tiles required splitting and downsizing before labeling could begin.
• Strict Output Requirements: The customer required the labeled data in a specific, non-generic format for direct use in their SAR detection models.

MLtwist’s Solution: A Precision SAR Annotation Pipeline

MLtwist designed a tailored workflow that addressed both the technical complexity and high throughput demands:

• Automated SAR Tile Splitting & Resizing: Large SAR image tiles were systematically broken down into smaller, consistent annotation-ready images.
• Tilted Bounding Box Tooling: AI-assisted annotation tools enabled fast and precise placement of angled bounding boxes, closely following the contours of each ship.
• High-Density Image Handling: Optimized workflows allowed for annotating 100+ ships per image without slowing throughput.
• Custom Data Formatting: Final datasets were delivered exactly in the customer’s required format—eliminating the need for post-processing.
• End-to-End Traceability: Every annotation was version-controlled, ensuring full transparency and repeatability.

Impact & Benefits

• 30% Reduction in Annotation Time: Project timelines were cut from months to weeks despite the high object density per SAR image.
• Higher Model Accuracy: Tilted bounding boxes provided more realistic spatial alignment, improving vessel detection in SAR model training.
• Zero Pre and Post-Processing Needed: Direct delivery in the desired format accelerated model integration.

The Takeaway

By combining automated SAR tile preprocessing, precision-oriented annotation tools, and customized output delivery, MLtwist enabled the rapid creation of high-quality labeled datasets for maritime vessel detection. This streamlined approach ensured scalability without sacrificing accuracy—helping improve real-world performance in SAR-based AI detection systems.