# Vision-Language Modeling with Regularized Spatial Transformer Networks for All Weather Crosswind Landing of Aircraft

arXiv: https://arxiv.org/abs/2405.05574 · HTML: https://mlsanity.com/paper/2405.05574/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 35 references that are in the map.
- First posted on arXiv 9 May 2024.

## Builds on (most cited first, 20 of 20)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [Variational Diffusion Models](https://mlsanity.com/paper/2107.00630/) (2021)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [Self-Supervised Learning of Pretext-Invariant Representations](https://mlsanity.com/paper/1912.01991/) (2019)
- [Deeply-Supervised Nets](https://mlsanity.com/paper/1409.5185/) (2014)
- [Spatial-Temporal Transformer Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2001.02908/) (2020)
- [ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing](https://mlsanity.com/paper/1803.01837/) (2018)
- [LaneNet: Real-Time Lane Detection Networks for Autonomous Driving](https://mlsanity.com/paper/1807.01726/) (2018)
- [Weather GAN: Multi-Domain Weather Translation Using Generative Adversarial Networks](https://mlsanity.com/paper/2103.05422/) (2021)
- [LARD -- Landing Approach Runway Detection -- Dataset for Vision Based Landing](https://mlsanity.com/paper/2304.09938/) (2023)
- [3D Morphable Models as Spatial Transformer Networks](https://mlsanity.com/paper/1708.07199/) (2017)

## Abstract (arXiv)

The intrinsic capability of the Human Vision System (HVS) to perceive depth of field and failure of Instrument Landing Systems (ILS) stimulates a pilot to perform a vision-based manual landing over an autoland approach. However, harsh weather creates challenges, and a pilot must have a clear view of runway elements before the minimum decision altitude. To aid in manual landing, a vision-based system trained to clear weather-induced visual degradations requires a robust landing dataset under various climatic conditions. Nevertheless, to acquire a dataset, flying an aircraft in dangerous weather impacts safety. Also, this system fails to generate reliable warnings, as localization of runway elements suffers from projective distortion while landing at crosswind. To combat, we propose to synthesize harsh weather landing images by training a prompt-based climatic diffusion network. Also, we optimize a weather distillation model using a novel diffusion-distillation loss to learn to clear these visual degradations. Precisely, the distillation model learns an inverse relationship with the diffusion network. Inference time, pre-trained distillation network directly clears weather-impacted onboard camera images, which can be further projected to display devices for improved visibility.Then, to tackle crosswind landing, a novel Regularized Spatial Transformer Networks (RuSTaN) module accurately warps landing images. It minimizes the localization error of runway object detector and helps generate reliable internal software warnings. Finally, we curated an aircraft landing dataset (AIRLAD) by simulating a landing scenario under various weather degradations and experimentally validated our contributions.