Multimodal image fusion for enhanced vehicle identification in intelligent transport
Naif Al Mudawi, Muhammad Waqas Ahmed, Haifa F. Alhasson, Naif S. Alshassari, Abdulwahab Alazeb, Mohammed Alshehri, Bayan Alabdullah
TL;DR
This paper introduces a deep learning-based multimodal image fusion method to improve vehicle detection in aerial imagery for intelligent transport systems.
Contribution
A novel attention-based depth map generation and hybrid feature extraction technique for enhanced aerial vehicle detection.
Findings
The proposed method achieved 98.4% precision on the Roundabout Aerial dataset for vehicle detection.
Hybrid feature extraction using HOG and BRISK in ViT improved detection performance over existing methods.
The model outperformed state-of-the-art approaches on three benchmark aerial datasets.
Abstract
Target detection in remote sensing is essential for applications such as law enforcement, military surveillance, and search-and-rescue. With advancements in computational power, deep learning methods have excelled in processing unimodal aerial imagery. The availability of diverse imaging modalities including, infrared, hyperspectral, multispectral, synthetic aperture radar, and Light Detection and Ranging (LiDAR) allows researchers to leverage complementary data sources. Integrating these multi-modal datasets has significantly enhanced detection performance, making these technologies more effective in real-world scenarios. In this work, we propose a novel approach that employs a deep learning-based attention mechanism to generate depth maps from aerial images. These depth maps are fused with RGB images to achieve enhanced feature representation. For image segmentation, we use Markov…
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Taxonomy
TopicsAdvanced Neural Network Applications · Video Surveillance and Tracking Methods · Autonomous Vehicle Technology and Safety