Domain-adaptive thermal object detection plays a key role in facilitating
visible (RGB)-to-thermal (IR) adaptation by reducing the need for co-registered
image pairs and minimizing reliance on large annotated IR datasets. Tuttavia,
inherent limitations of IR images, such as the lack of color and texture cues,
pose challenges for RGB-trained models, leading to increased false positives
and poor-quality pseudo-labels. To address this, we propose Semantic-Aware Gray
color Augmentation (SAGA), a novel strategy for mitigating color bias and
bridging the domain gap by extracting object-level features relevant to IR
images. Additionally, to validate the proposed SAGA for drone imagery, we
introduce the IndraEye, a multi-sensor (RGB-IR) dataset designed for diverse
applications. The dataset contains 5,612 images with 145,666 instances,
captured from diverse angles, altitudes, backgrounds, and times of day,
offering valuable opportunities for multimodal learning, domain adaptation for
object detection and segmentation, and exploration of sensor-specific strengths
and weaknesses. IndraEye aims to enhance the development of more robust and
accurate aerial perception systems, especially in challenging environments.
Experimental results show that SAGA significantly improves RGB-to-IR adaptation
for autonomous driving and IndraEye dataset, achieving consistent performance
gains of +0.4% to +7.6% (mAP) when integrated with state-of-the-art domain
adaptation techniques. The dataset and codes are available at
https://github.com/airliisc/IndraEye.

Questo articolo esplora i giri e le loro implicazioni.

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