# Enhancing Maritime Object Detection in Real-Time with RT-DETR and Data Augmentation

arXiv: https://arxiv.org/abs/2510.07346 · HTML: https://mlsanity.com/paper/2510.07346/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 7 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [Night-to-Day Image Translation for Retrieval-based Localization](https://mlsanity.com/paper/1809.09767/) (2018)
- [Video Processing from Electro-optical Sensors for Object Detection and Tracking in Maritime Environment: A Survey](https://mlsanity.com/paper/1611.05842/) (2016)
- [Neptune-X: Active X-to-Maritime Generation for Universal Maritime Object Detection](https://mlsanity.com/paper/2509.20745/) (2025)
- [Enhancing object detection robustness: A synthetic and natural perturbation approach](https://mlsanity.com/paper/2304.10622/) (2023)
- [SafeSea: Synthetic Data Generation for Adverse & Low Probability Maritime Conditions](https://mlsanity.com/paper/2311.14764/) (2023)
- [Small Object Detection by DETR via Information Augmentation and Adaptive Feature Fusion](https://mlsanity.com/paper/2401.08017/) (2024)

## Built on by (most cited first, 2 of 2)

- [Comparative Analysis of Data Augmentation for Clinical ECG Classification with STAR](https://mlsanity.com/paper/2510.24740/) (2025)
- [Privacy-Aware Federated nnU-Net for ECG Page Digitization](https://mlsanity.com/paper/2510.22387/) (2025)

## Abstract (arXiv)

Maritime object detection faces essential challenges due to the small target size and limitations of labeled real RGB data. This paper will present a real-time object detection system based on RT-DETR, enhanced by employing augmented synthetic images while strictly evaluating on real data. This study employs RT-DETR for the maritime environment by combining multi-scale feature fusion, uncertainty-minimizing query selection, and smart weight between synthetic and real training samples. The fusion module in DETR enhances the detection of small, low-contrast vessels, query selection focuses on the most reliable proposals, and the weighting strategy helps reduce the visual gap between synthetic and real domains. This design preserves DETR's refined end-to-end set prediction while allowing users to adjust between speed and accuracy at inference time. Data augmentation techniques were also used to balance the different classes of the dataset to improve the robustness and accuracy of the model. Regarding this study, a full Python robust maritime detection pipeline is delivered that maintains real-time performance even under practical limits. It also verifies how each module contributes, and how the system handles failures in extreme lighting or sea conditions. This study also includes a component analysis to quantify the contribution of each architectural module and explore its interactions.