FID-YOLO: A pedestrian detection model integrating multispectral information in complex environments
Di Yang, Xilong Zhang, Peng Wang
TL;DR
This paper introduces FID-YOLO, a new pedestrian detection model that improves accuracy in complex environments by combining visible and infrared images.
Contribution
The novel FID-YOLO model integrates multispectral data using an illumination-aware fusion module and a scale-adaptive detection head.
Findings
FID-YOLO outperforms benchmark models on the M3FD and LLVIP datasets.
Ablation experiments confirm the importance of each proposed module.
The model improves detection accuracy in adverse conditions like occlusions and scale variations.
Abstract
The advancement of pedestrian detection technology is of great importance for various applications such as intelligent driving, object tracking, and robot navigation. Many studies in this field have demonstrated that image quality significantly contributes to the precision of detection. However, unexpected factors such as adverse weather, occlusions, and scale variations, which extremely weaken the main features of the detected objects, leading to a decrease in detection accuracy. To address these problems, we propose a Feature-enriched Image Detection-YOLO (FID-YOLO), to improve pedestrian detection performance in complex environments by integrating visible and infrared light information. Specifically, we design an illumination-aware image fusion module for visible and infrared image information fusion to generate a new image within more information to enrich pedestrian features. Then,…
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Taxonomy
TopicsAdvanced Neural Network Applications · Video Surveillance and Tracking Methods · Visual Attention and Saliency Detection