C2Former: Calibrated and Complementary Transformer for RGB-Infrared Object Detection

Główne pojęcia

Proposing C2Former to address modality miscalibration and fusion imprecision in RGB-IR object detection.

Streszczenie

Object detection using RGB and IR images is crucial for around-the-clock applications. Existing methods face challenges of modality miscalibration and fusion imprecision. C2Former addresses these issues with an Inter-modality Cross-Attention module and Adaptive Feature Sampling. Experimental results show improved detection accuracy on DroneVehicle and KAIST datasets.

Statystyki

Extensive experiments conducted on DroneVehicle and KAIST RGB-IR datasets.
C2Former achieves 74.2% mAP, outperforming other multispectral detection methods.
Computational cost reduced from 139.8G FLOPs to 100.9G FLOPs with the addition of AFS module.

Cytaty

"In C2Former, we design an Inter-modality Cross-Attention (ICA) module to obtain the calibrated and complementary features by learning the cross-attention relationship between the RGB and IR modality."
"With extensive experiments on the DroneVehicle and KAIST RGB-IR datasets, we verify that our method can fully utilize the RGB-IR complementary information."

Kluczowe wnioski z

$\mathbf{C}^2$Former

by Maoxun Yuan,... o arxiv.org 03-14-2024

https://arxiv.org/pdf/2306.16175.pdf

Głębsze pytania

How does C2Former compare to other state-of-the-art methods in terms of computational efficiency

C2Former demonstrates superior computational efficiency compared to other state-of-the-art methods in the field of RGB-IR object detection. When comparing C2Former with TSFADet, a similar plug-in module-based method, C2Former shows a significant reduction in Floating Point Operations (FLOPs). Specifically, while TSFADet requires 109.8G FLOPs for its operations, C2Former only needs 89.9G FLOPs. This reduction in computational cost showcases the efficiency of C2Former in optimizing resource utilization without compromising on performance.

What potential applications beyond object detection could benefit from the technology used in C2Former

The technology used in C2Former can have applications beyond object detection that could benefit various fields. One potential application is environmental monitoring using satellite imagery. By incorporating multispectral data from satellites and applying techniques like feature alignment and fusion as seen in C2Former, researchers can enhance their analysis of environmental changes over time. This could aid in tracking deforestation patterns, monitoring agricultural developments, or assessing natural disasters more effectively.

How might advancements in multispectral object detection impact fields like autonomous driving or surveillance systems

Advancements in multispectral object detection facilitated by technologies like C2Former have the potential to revolutionize fields such as autonomous driving and surveillance systems. In autonomous driving, improved object detection capabilities using RGB-IR fusion can enhance vehicle perception under challenging conditions like low light or adverse weather situations where traditional sensors may struggle. For surveillance systems, the ability to accurately detect objects across different spectral bands can lead to better security measures by providing enhanced visibility during both day and night scenarios.