Edge-guided Multi-domain RGB-to-TIR image Translation for Training Vision Tasks with Challenging Labels (2301.12689v1)

Abstract: The insufficient number of annotated thermal infrared (TIR) image datasets not only hinders TIR image-based deep learning networks to have comparable performances to that of RGB but it also limits the supervised learning of TIR image-based tasks with challenging labels. As a remedy, we propose a modified multidomain RGB to TIR image translation model focused on edge preservation to employ annotated RGB images with challenging labels. Our proposed method not only preserves key details in the original image but also leverages the optimal TIR style code to portray accurate TIR characteristics in the translated image, when applied on both synthetic and real world RGB images. Using our translation model, we have enabled the supervised learning of deep TIR image-based optical flow estimation and object detection that ameliorated in deep TIR optical flow estimation by reduction in end point error by 56.5\% on average and the best object detection mAP of 23.9\% respectively. Our code and supplementary materials are available at https://github.com/rpmsnu/sRGB-TIR.

Analysis of Edge-guided Multi-domain RGB-to-TIR Image Translation for Vision Tasks

This paper presents a novel approach for the translation of RGB images to Thermal Infrared (TIR) images within the context of computer vision tasks that require annotated datasets. The primary motivation stems from the inadequacy of annotated TIR image datasets, which restricts the training of effective TIR image-based models through supervised learning. The authors propose a modified multi-domain RGB to TIR image translation model that notably emphasizes edge preservation, thereby ensuring that prominent features from the original RGB images are retained and correctly represented in the translated TIR images.

Summary and Numerical Results

The proposed model leans on a multi-domain translation network with disentangled content and style latent vectors to address the task of RGB to TIR translation. This architecture is predicated on the use of adaptive instance normalization and is rigorously trained using a mix of adversarial loss and style-augmented cyclic loss, complemented by reconstruction losses dedicated to content and style evaluation. Moreover, the introduction of a Laplace of Gaussian (LoG) loss serves as a key innovation, directing the network towards preserving structural and edge-based details during translation.

Strong quantitative outcomes underscore the efficacy of this approach. The model yielded significant improvements in tasks reliant on accurate optical flow estimation and object detection within TIR imagery. Specifically, the application of the model in supervised learning configurations led to a substantial average end-point error reduction of 56.5% in TIR optical flow estimation, a noteworthy enhancement in performance. Concurrently, the highest mean Average Precision (mAP) achieved in object detection tasks reached 23.9%, reflecting robustness in practical applications.

Key Contributions and Implications

This research presents several pioneering contributions to the field of computer vision, especially regarding TIR imaging:

1. Edge-guided Translation Network: The paper introduces a robust edge-guided and style-controlled translation model that mitigates typical errors like artifacts by optimally selecting suitable style codes. This advancement facilitates more faithful translations of RGB images to TIR by maintaining structural consistency, even in challenging translation scenarios such as night-time settings.
2. Supervised Learning of Challenging Tasks: The methodology exemplifies a breakthrough in enabling supervised learning for complex tasks such as TIR-based optical flow estimation, which typically translate poorly due to labeling difficulties. By employing the translated datasets for training, the authors highlight a reduction in manual effort required for dataset annotation.
3. Flexibility and Generalization: The generalization capability of the proposed method renders it applicable to real-world scenarios with both synthetic and real RGB imagery, illustrating not only the feasibility but also the scalability of the approach for broader applications in robotics and autonomous systems.

The theoretical implications of this paper revolve around the effective disentanglement of content and style domains in image translation, offering a nuanced mechanism to address multi-modal data disparities. From a practical standpoint, this work could expedite developments in autonomous vehicles and surveillance systems where TIR cameras are deployed under diverse environmental conditions.

Future Directions

Future research could delve into refining the translation results to further reduce artifacts and improve performance consistency across a variety of settings. Additionally, expanding the model's application to encompass other vision tasks such as high-resolution semantic segmentation or three-dimensional object detection could uncover more utilitarian benefits. Moreover, the integration of more sophisticated style selection strategies, potentially influenced by recent advancements in contrastive learning, may enhance the adaptability of the model to uncharted domain shifts. Thus, while the current research offers a robust foundation, considerable scope exists for enhancing and broadening its applicability in the diverse spectrum of computer vision challenges.