SDRSwin: A Residual Swin Transformer Network with Saliency Detection for Infrared and Visible Image Fusion

Abstract

Infrared and visible image fusion is a solution that generates an information-rich individual image with different modal information by fusing images obtained from various sensors. Salient detection can better emphasize the targets of concern. We propose a residual Swin Transformer fusion network based on saliency detection, termed SDRSwin, aiming to highlight the salient thermal targets in the infrared image while maintaining the texture details in the visible image. The SDRSwin network is trained with a two-stage training approach. In the first stage, we train an encoder–decoder network based on residual Swin Transformers to achieve powerful feature extraction and reconstruction capabilities. In the second stage, we develop a novel salient loss function to guide the network to fuse the salient targets in the infrared image and the background detail regions in the visible image. The extensive results indicate that our method has abundant texture details with clear bright infrared targets and achieves a better performance than the twenty-one state-of-the-art methods in both subjective and objective evaluation.

Methodology for Thermal Analysis of Combustion Engines, Applied to Automobiles

“…These images are analyzed in different devices, some in the same equipment, others in computer devices that analyze the images, after their acquisition, which resorts to the use of computer equipment and other processor structures, these solutions are chosen depending on the use and application [6]. As well as we find solutions that record images, it is also important to use sensors to analyze the temperature at a particular point, for these tasks it is resorted to the use of sensors and data transmission lines, for which a high transmission rate is required to identify a record in real time [7,8].…”

Methodology for the Analysis of the Operation of High-Performance Computing Equipment

“…There are applications where it is necessary to analyze critical temperature points, as is the case of applications where temperature sensors are used, these collaborate in the specific analysis of solutions where a critical factor is temperature, and we find solutions where networks are used to connect multiple sensors and all of them analyzing the temperature. Other works carry out analysis after acquisition, where they use computer equipment to analyze the images, others work directly on the acquisition devices, as is the case with processors and FPGAs, for which it is necessary to program the processing in the same components [6][7][8].…”