Comparing Apples and Oranges: Off-Road Pedestrian Detection on the NREC Agricultural Person-Detection Dataset

Pezzementi, Zachary; Tabor, Trenton; Hu, Pengxiang; Chang, Jonathan; Ramanan, Deva; Wellington, Carl; Babu, Benzun Pious Wisely; Herman, Herman

doi:10.48550/arxiv.1707.07169

Cited by 1 publication

(1 citation statement)

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“…In this paper, the NREC Person Detection Dataset [54], designed for detecting person objects in off-road environments, was utilized as the background image. The source datasets included the Fall Detection Dataset [55], the Fall Detection Dataset [56], and the UR Fall Detection Dataset [57], encompassing behavioral datasets with both fallen and standing person.…”

Section: Methodsmentioning

confidence: 99%

Automated Region of Interest-Based Data Augmentation for Fallen Person Detection in Off-Road Autonomous Agricultural Vehicles

Baek,

Yu,

Son

et al. 2024

Sensors

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Due to the global population increase and the recovery of agricultural demand after the COVID-19 pandemic, the importance of agricultural automation and autonomous agricultural vehicles is growing. Fallen person detection is critical to preventing fatal accidents during autonomous agricultural vehicle operations. However, there is a challenge due to the relatively limited dataset for fallen persons in off-road environments compared to on-road pedestrian datasets. To enhance the generalization performance of fallen person detection off-road using object detection technology, data augmentation is necessary. This paper proposes a data augmentation technique called Automated Region of Interest Copy-Paste (ARCP) to address the issue of data scarcity. The technique involves copying real fallen person objects obtained from public source datasets and then pasting the objects onto a background off-road dataset. Segmentation annotations for these objects are generated using YOLOv8x-seg and Grounded-Segment-Anything, respectively. The proposed algorithm is then applied to automatically produce augmented data based on the generated segmentation annotations. The technique encompasses segmentation annotation generation, Intersection over Union-based segment setting, and Region of Interest configuration. When the ARCP technique is applied, significant improvements in detection accuracy are observed for two state-of-the-art object detectors: anchor-based YOLOv7x and anchor-free YOLOv8x, showing an increase of 17.8% (from 77.8% to 95.6%) and 12.4% (from 83.8% to 96.2%), respectively. This suggests high applicability for addressing the challenges of limited datasets in off-road environments and is expected to have a significant impact on the advancement of object detection technology in the agricultural industry.

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Section: Methodsmentioning

confidence: 99%