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<p>Designed to address the challenge of identifying water surface floating objects with transparent features under complex backgrounds such as irritating light sources, we introduce Edge-Aware Detection Transformer(EA-DETR), a novel end-to-end object detection model based on DINO-ResNet50. Three innovative strategies are mainly incorporated. First, Edge-Aware Denoising(EAD) Module, composed of convolutions and Squeeze-and-Excitation(SE) Module, accentuates boundary information across multi-scale features after Backbone of ResNet50. Secondly, Mamba-like Linear Attention(MLLA) Module continuously suppresses incorrect positional weights while emphasizing correct ones through linear operations in Transformer Encoder layers. Finally, a dynamic matching approach combines flexible coefficients for CIoU, a regularization item based on bounding box sizes and a frame utilizing Sober operator to convert coordinated into edges, thus capturing more detailed information of transparent objects by calculating extra segmentation mask loss fostered by boundaries. EA-DETR achieves 72.4% AP(0.5:0.95), 98.7% AP(0.5) and 65.7% AR(0.5:0.95) on the FloW-ImG Dataset, significantly outperforming other DETR-like models. Furthermore, it demonstrates ideal generalization and robustness, with results on the Trash_ICRA19 Dataset exhibiting 44.1% AP(0.5:0.95), 74.2% AP(0.5), and 64.5% AR(0.5:0.95). Overall, the architecture proposed can provide effective tools for learning ambiguous boundary features without disturbance of extreme lightness caused by water surface and the sun.</p>

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EA-DETR: Edge-Aware Detection Transformer for Water Surface Floating Object Identification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月2日

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  • Jiayi Wang,
  • Xiangyang Liu

摘要

Designed to address the challenge of identifying water surface floating objects with transparent features under complex backgrounds such as irritating light sources, we introduce Edge-Aware Detection Transformer(EA-DETR), a novel end-to-end object detection model based on DINO-ResNet50. Three innovative strategies are mainly incorporated. First, Edge-Aware Denoising(EAD) Module, composed of convolutions and Squeeze-and-Excitation(SE) Module, accentuates boundary information across multi-scale features after Backbone of ResNet50. Secondly, Mamba-like Linear Attention(MLLA) Module continuously suppresses incorrect positional weights while emphasizing correct ones through linear operations in Transformer Encoder layers. Finally, a dynamic matching approach combines flexible coefficients for CIoU, a regularization item based on bounding box sizes and a frame utilizing Sober operator to convert coordinated into edges, thus capturing more detailed information of transparent objects by calculating extra segmentation mask loss fostered by boundaries. EA-DETR achieves 72.4% AP(0.5:0.95), 98.7% AP(0.5) and 65.7% AR(0.5:0.95) on the FloW-ImG Dataset, significantly outperforming other DETR-like models. Furthermore, it demonstrates ideal generalization and robustness, with results on the Trash_ICRA19 Dataset exhibiting 44.1% AP(0.5:0.95), 74.2% AP(0.5), and 64.5% AR(0.5:0.95). Overall, the architecture proposed can provide effective tools for learning ambiguous boundary features without disturbance of extreme lightness caused by water surface and the sun.