Object recognition in atmospheric turbulence scenes

TL;DR

This paper introduces a novel deep learning framework utilizing deformable convolutions and feature pyramids to improve object detection in atmospheric turbulence scenes, outperforming benchmarks on synthetic and real data.

Contribution

The paper presents a new method combining deformable convolutions with Faster R-CNN for robust object detection under atmospheric turbulence conditions.

Findings

Achieved over 30% mAP on synthetic VOC dataset.

Significant performance improvement on real turbulence data.

Outperforms existing benchmark methods.

Abstract

The influence of atmospheric turbulence on acquired surveillance imagery poses significant challenges in image interpretation and scene analysis. Conventional approaches for target classification and tracking are less effective under such conditions. While deep-learning-based object detection methods have shown great success in normal conditions, they cannot be directly applied to atmospheric turbulence sequences. In this paper, we propose a novel framework that learns distorted features to detect and classify object types in turbulent environments. Specifically, we utilise deformable convolutions to handle spatial turbulent displacement. Features are extracted using a feature pyramid network, and Faster R-CNN is employed as the object detector. Experimental results on a synthetic VOC dataset demonstrate that the proposed framework outperforms the benchmark with a mean Average Precision (mAP) score exceeding 30%. Additionally, subjective results on real data show significant improvement in performance.