Dynamic Adaptive Multispectral Detection Transformer for Infrared-Visible Object Detection

Core Concepts

The author proposes the DAMS-DETR model to address challenges in infrared-visible object detection by dynamically selecting modality features and adaptively fusing complementary information.

Abstract

The DAMS-DETR model addresses challenges in infrared-visible object detection by proposing a Modality Competitive Query Selection strategy and a Multispectral Deformable Cross-attention module. The method outperforms state-of-the-art models on various datasets, demonstrating its effectiveness in handling complex scenes and misalignment issues. The paper discusses the importance of infrared-visible object detection due to its ability to capture objects under challenging conditions like low illumination or smoke. It introduces the DAMS-DETR model based on DETR, aiming to fuse complementary information from infrared and visible images effectively. Key components of DAMS-DETR include Modality Competitive Query Selection for dynamic feature selection and a Multispectral Deformable Cross-attention module for adaptive feature fusion. Experiments on different datasets show significant improvements over existing methods. The study highlights the challenges of modality interference and misalignment in infrared-visible object detection, emphasizing the need for adaptive strategies like those proposed in DAMS-DETR. The model's performance is evaluated across multiple scenarios, showcasing its robustness and efficiency.

Stats

Experiments on four public datasets demonstrate significant improvements compared to other state-of-the-art methods. Our method achieves better results with mAP50: 80.2%, mAP75: 56.0%, mAP: 52.9% on the M3FD dataset. On the FLIR-aligned dataset, our method achieves mAP50: 86.6%, mAP75: 48.1%, mAP: 49.3%. Results on the LLVIP dataset show mAP50: 97.9%, mAP75: 79.1%, mAP: 69.6%. For the VEDAI dataset, our method achieves mAP50: 91.5%, mAP: 55.3%.

Quotes

"The one with the green bounding box has good complementary information in both modalities." "Some works learn global fusion weight to adapt to specific scenes." "Our method can adaptively focus on dominant modalities and effectively mine fine-grained multi-level semantic complementary information."

Key Insights Distilled From

DAMS-DETR

by Guo Junjie,G... at arxiv.org 03-04-2024

https://arxiv.org/pdf/2403.00326.pdf

Deeper Inquiries

How does DAMS-DETR handle extreme misalignment situations?

DAMS-DETR addresses extreme misalignment situations by incorporating a Multispectral Deformable Cross-attention module in its architecture. This module allows the network to adaptively sample and aggregate multi-semantic level features from both infrared and visible images, even when objects are significantly misaligned between modalities. By using 4D reference points to constrain sampling ranges within the cross-attention module, DAMS-DETR can focus on key information around objects despite misalignments. Additionally, the network iteratively refines queries and anchor boxes through cascaded decoder layers, enabling it to handle extreme misalignment scenarios more effectively.

What are potential limitations of using transformer-based models like DETR for small object detection?

One potential limitation of using transformer-based models like DETR for small object detection is their prioritization of global information over local details. Transformers are known for capturing long-range dependencies efficiently but may struggle with extracting precise boundaries or intricate features required for accurately detecting small objects. As a result, transformer-based models might not perform as well as CNN-based detectors in tasks that involve detecting small objects where fine-grained details are crucial.

How can adaptive strategies like Modality Competitive Query Selection be applied in other computer vision tasks beyond object detection?

Adaptive strategies like Modality Competitive Query Selection can be applied in various computer vision tasks beyond object detection to enhance performance and robustness. For instance: Semantic Segmentation: Prioritizing dominant modalities based on scene characteristics could improve segmentation accuracy. Image Classification: Selecting modality-specific features dynamically could help classify images better under varying conditions. Instance Segmentation: Adaptive feature selection based on complementary information could aid in accurately segmenting instances across different modalities. By incorporating similar adaptive strategies tailored to specific task requirements, computer vision systems can benefit from improved adaptability and efficiency across a range of applications beyond just object detection.