Application of image-to-image translation in improving pedestrian detection

• URL: http://arxiv.org/abs/2209.03625v1
• Date: Thu, 8 Sep 2022 08:07:01 GMT
• Title: Application of image-to-image translation in improving pedestrian detection
• Authors: Devarsh Patel, Sarthak Patel, Megh Patel
• Abstract summary: In this study we are going to use advanced deep learning models like pix2pixGAN and YOLOv7 on LLVIP dataset, containing visible-infrared image pairs for low light vision. This dataset contains 33672 images and most of the images were captured in dark scenes, tightly synchronized with time and location.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The lack of effective target regions makes it difficult to perform several visual functions in low intensity light, including pedestrian recognition, and image-to-image translation. In this situation, with the accumulation of high-quality information by the combined use of infrared and visible images it is possible to detect pedestrians even in low light. In this study we are going to use advanced deep learning models like pix2pixGAN and YOLOv7 on LLVIP dataset, containing visible-infrared image pairs for low light vision. This dataset contains 33672 images and most of the images were captured in dark scenes, tightly synchronized with time and location.

Related papers

• Beyond Night Visibility: Adaptive Multi-Scale Fusion of Infrared and Visible Images [49.75771095302775]
  We propose an Adaptive Multi-scale Fusion network (AMFusion) with infrared and visible images. First, we separately fuse spatial and semantic features from infrared and visible images, where the former are used for the adjustment of light distribution. Second, we utilize detection features extracted by a pre-trained backbone that guide the fusion of semantic features. Third, we propose a new illumination loss to constrain fusion image with normal light intensity.
  arXiv  Detail & Related papers  (2024-03-02T03:52:07Z)
• NiteDR: Nighttime Image De-Raining with Cross-View Sensor Cooperative Learning for Dynamic Driving Scenes [49.92839157944134]
  In nighttime driving scenes, insufficient and uneven lighting shrouds the scenes in darkness, resulting degradation of image quality and visibility. We develop an image de-raining framework tailored for rainy nighttime driving scenes. It aims to remove rain artifacts, enrich scene representation, and restore useful information.
  arXiv  Detail & Related papers  (2024-02-28T09:02:33Z)
• You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement [50.37253008333166]
  Low-Light Image Enhancement (LLIE) task tends to restore the details and visual information from corrupted low-light images. We propose a novel trainable color space, named Horizontal/Vertical-Intensity (HVI) It not only decouples brightness and color from RGB channels to mitigate the instability during enhancement but also adapts to low-light images in different illumination ranges due to the trainable parameters.
  arXiv  Detail & Related papers  (2024-02-08T16:47:43Z)
• Nighttime Thermal Infrared Image Colorization with Feedback-based Object Appearance Learning [27.58748298687474]
  We propose a generative adversarial network incorporating feedback-based object appearance learning (FoalGAN) FoalGAN is effective for appearance learning of small objects, but also outperforms other image translation methods in terms of semantic preservation and edge consistency.
  arXiv  Detail & Related papers  (2023-10-24T09:59:55Z)
• Enhancing Low-Light Images Using Infrared-Encoded Images [81.8710581927427]
  Previous arts mainly focus on the low-light images captured in the visible spectrum using pixel-wise loss. We propose a novel approach to increase the visibility of images captured under low-light environments by removing the in-camera infrared (IR) cut-off filter.
  arXiv  Detail & Related papers  (2023-07-09T08:29:19Z)
• Enhancing Low-Light Images in Real World via Cross-Image Disentanglement [58.754943762945864]
  We propose a new low-light image enhancement dataset consisting of misaligned training images with real-world corruptions. Our model achieves state-of-the-art performances on both the newly proposed dataset and other popular low-light datasets.
  arXiv  Detail & Related papers  (2022-01-10T03:12:52Z)
• LLVIP: A Visible-infrared Paired Dataset for Low-light Vision [4.453060631960743]
  We present LLVIP, a visible-infrared paired dataset for low-light vision. This dataset contains 30976 images, or 15488 pairs, most of which were taken at very dark scenes. We compare the dataset with other visible-infrared datasets and evaluate the performance of some popular visual algorithms.
  arXiv  Detail & Related papers  (2021-08-24T16:29:17Z)
• Thermal Infrared Image Colorization for Nighttime Driving Scenes with Top-Down Guided Attention [14.527765677864913]
  We propose a toP-down attEntion And gRadient aLignment based GAN, referred to as PearlGAN. A top-down guided attention module and an elaborate attentional loss are first designed to reduce the semantic encoding ambiguity during translation. In addition, pixel-level annotation is carried out on a subset of FLIR and KAIST datasets to evaluate the semantic preservation performance of multiple translation methods.
  arXiv  Detail & Related papers  (2021-04-29T14:35:25Z)
• The Cube++ Illumination Estimation Dataset [50.58610459038332]
  A new illumination estimation dataset is proposed in this paper. It consists of 4890 images with known illumination colors as well as with additional semantic data. The dataset can be used for training and testing of methods that perform single or two-illuminant estimation.
  arXiv  Detail & Related papers  (2020-11-19T18:50:08Z)
• Towards Geometry Guided Neural Relighting with Flash Photography [26.511476565209026]
  We propose a framework for image relighting from a single flash photograph with its corresponding depth map using deep learning. We experimentally validate the advantage of our geometry guided approach over state-of-the-art image-based approaches in intrinsic image decomposition and image relighting.
  arXiv  Detail & Related papers  (2020-08-12T08:03:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.