Exploiting the Potential of Overlapping Cropping for Real-World Pedestrian and Vehicle Detection with Gigapixel-Level Images

Pedestrian and vehicle detection is widely used in intelligent assisted driving, pedestrian counting, drone aerial photography, and other applications. Recently, with the development of gigacameras, gigapixel-level images have emerged. The large field of view and high resolution provide global and l...

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Bibliographic Details
Published in:Applied sciences 2023-03, Vol.13 (6), p.3637
Main Authors: Wang, Chunlei, Feng, Wenquan, Liu, Binghao, Ling, Xinyang, Yang, Yifan
Format: Article
Language:English
Subjects:
Accuracy
Aerial photography
Algorithms
Classification
Datasets
Deep learning
Drone aircraft
gigapixel-level image
High resolution
pedestrian detection
Pedestrians
Sensors
super-high resolution
vehicle detection
Vehicle detectors
Vehicles
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Summary:Pedestrian and vehicle detection is widely used in intelligent assisted driving, pedestrian counting, drone aerial photography, and other applications. Recently, with the development of gigacameras, gigapixel-level images have emerged. The large field of view and high resolution provide global and local information, which enables object detection in real-world scenarios. Although existing pedestrian and vehicle detection algorithms have achieved remarkable success for standard images, their methods are not suitable for ultra-high-resolution images. In order to improve the performance of existing pedestrian and vehicle detectors in real-world scenarios, we used a sliding window to crop the original images to solve this problem. When fusing the sub-images, we proposed a midline method to reduce the cropped objects that NMS could not eliminate. At the same time, we used varifocal loss to solve the imbalance between positive and negative samples caused by the high resolution. We also found that pedestrians and vehicles were separable in size and comprised more than one target type. As a result, we improved the detector performance with single-class object detection for pedestrians and vehicles, respectively. At the same time, we provided many useful strategies to improve the detector. The experimental results demonstrated that our method could improve the performance of real-world pedestrian and vehicle detection.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13063637