Application of image-to-image translation in improving pedestrian detection
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 30 references that are in the map. First posted on arXiv 8 Sep 2022.
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References · 12
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2022Generative Adversarial Networkscited by 5,627
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
- 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 982
- 2021LLVIP: A Visible-infrared Paired Dataset for Low-light Visioncited by 231
- 2021Low-Light Image and Video Enhancement Using Deep Learning: A Surveycited by 148
- 2021MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?cited by 69
- 2022BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pixcited by 61
- 2016End-to-end training of object class detectors for mean average precisioncited by 35
- 2018Generative Adversarial Network for Medical Images (MI-GAN)cited by 17
- 2018Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyondcited by 13
- 2022Video object tracking based on YOLOv7 and DeepSORTcited by 3
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