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

• URL: http://arxiv.org/abs/2405.05574v1
• Date: Thu, 9 May 2024 06:48:42 GMT
• Title: Vision-Language Modeling with Regularized Spatial Transformer Networks for All Weather Crosswind Landing of Aircraft
• Authors: Debabrata Pal, Anvita Singh, Saumya Saumya, Shouvik Das,
• Abstract summary: In harsh weather, a pilot must have a clear view of runway elements before the minimum decision altitude. A vision-based system tailored to localize runway elements likewise gets affected, especially during crosswind. We propose to integrate a prompt-based climatic diffusion network with a weather distillation model using a novel diffusion-distillation loss.
• Score: 0.3749861135832073
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The intrinsic capability to perceive depth of field and extract salient information by the Human Vision System (HVS) stimulates a pilot to perform manual landing over an autoland approach. However, harsh weather creates visibility hindrances, and a pilot must have a clear view of runway elements before the minimum decision altitude. To help a pilot in manual landing, a vision-based system tailored to localize runway elements likewise gets affected, especially during crosswind due to the projective distortion of aircraft camera images. To combat this, we propose to integrate a prompt-based climatic diffusion network with a weather distillation model using a novel diffusion-distillation loss. Precisely, the diffusion model synthesizes climatic-conditioned landing images, and the weather distillation model learns inverse mapping by clearing those visual degradations. Then, to tackle the crosswind landing scenario, a novel Regularized Spatial Transformer Networks (RuSTaN) learns to accurately calibrate for projective distortion using self-supervised learning, which minimizes localization error by the downstream runway object detector. Finally, we have simulated a clear-day landing scenario at the busiest airport globally to curate an image-based Aircraft Landing Dataset (AIRLAD) and experimentally validated our contributions using this dataset to benchmark the performance.

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