Increasing the Efficiency of DETR for Maritime High-Resolution Images

• URL: http://arxiv.org/abs/2605.10269v1
• Date: Mon, 11 May 2026 09:31:12 GMT
• Title: Increasing the Efficiency of DETR for Maritime High-Resolution Images
• Abstract summary: Maritime object detection is critical for the safe navigation of unmanned surface vessels (USVs)<n>Real-time detection is challenging due to long distances, small object sizes, large-scale variations, edge computing limitations, and the high memory demands of high-resolution imagery.<n>We leverage Vision Mamba (ViM) backbones to capture long-range dependencies while scaling linearly with sequence length.<n>Our approach achieves a better balance between performance and computational efficiency in maritime object detection.
• Score: 4.035972767997811
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Maritime object detection is critical for the safe navigation of unmanned surface vessels (USVs), requiring accurate recognition of obstacles from small buoys to large vessels. Real-time detection is challenging due to long distances, small object sizes, large-scale variations, edge computing limitations, and the high memory demands of high-resolution imagery. Existing solutions, such as downsampling or image splitting, often reduce accuracy or require additional processing, while memory-efficient models typically handle only limited resolutions. To overcome these limitations, we leverage Vision Mamba (ViM) backbones, which build on State Space Models (SSMs) to capture long-range dependencies while scaling linearly with sequence length. Images are tokenized into sequences for efficient high-resolution processing. For further computational efficiency, we design a tailored Feature Pyramid Network with successive downsampling and SSM layers, as well as token pruning to reduce unnecessary computation on background regions. Compared to state-of-the-art methods like RT-DETR with ResNet50 backbone, our approach achieves a better balance between performance and computational efficiency in maritime object detection.

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