Thermal infrared imaging has great potential for applications in auto-vehicle, human-machine interactive, surveillance, and defense systems owing to its performance in low-light and low-visible conditions. Nevertheless, the shortage of published datasets and benchmarks in the infrared domain has hampered the development of contemporary research. To tackle this problem, we propose a method to produce synthetic thermal infrared (TIR) images using a diffusion-based image-to-image translation model to enrich the data for learning. The model translates the abundantly available Red, Green, Blue (RGB) images to synthetic TIR data closer to the domain of authentic TIR images. For this purpose, we explore the usage of an unpaired image translation neural model based on Schrödinger bridge algorithms. In addition, the visual characteristic of the object in the image is an important consideration in generating results. Thus, we take advantage of a segmentation module before the image-to-image translation model to discriminate the background and object regions. We practice the model’s performance with a self-proposed dataset comprising unpairs realistic RGB-TIR images. We evaluated the model’s performance in synthesizing thermal images by comparing them to our original thermal dataset, achieving a Fréchet inception distance score of ∼80, indicative of high-quality image generation. In particular, the synthesized images of our model increase the classification accuracy by 15% when only realistic TIR images are used.
Identifying an object of interest in thermal images plays a vital role in several military and civilian applications. The deep learning approach has shown its superiority in object detection in various RGB datasets. However, regarding to thermal images, their low resolution and shortage of detail properties impose a huge challenge that hinders the accuracy. In this paper, we propose an improved version of YOLOv5 model to tackle this problem. Convolution Block Attention Module (CBAM) is integrated into traditional YOLOv5 for better representation of objects by focusing on important features and neglecting unnecessary ones. The Selective Kernel Network(SENet) is added to maximize the shallow features usage. Furthermore, the multiscale detection mechanism is utilized to improve small object detection accuracy. We train our model on the mixed visible-thermal images collected from LSOTB-TIR, LLVIP, and COCO datasets. We evaluate the performance of our method on 8 classes of objects: person, bicycle, airplane, helicopter, car, motorbike, boat, and tank. Experiment results show that our approach can achieve mAP up to 90.2%, which outperforms the original YOLOv5 and other popular methods.
Blurry images are not only visually unappealing, but they also degrade the performance of computer vision applications dramatically. As a result, motion deblurring for the thermal infrared picture plays a critical role in infrared systems. In recent years, convolutional neural network-based image deblurring methods have yielded promising performance with remarkable results and low computational cost. Inspired by these works, in this paper, we investigate an end-to-end deblurring model for single blurred thermal IR image by adopting the multi-input approach. Our model achieve PSNR and SSIM scores of 31.83 and 0.6435 when evaluating on our blur-sharp thermal infrared image pair dataset. Furthermore, the lightweight nature of our model allows it to operate at 140 FPS when inferring on Tesla V100 GPU.
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