Behavior Prediction Using 3D Box Estimation in Road Environment

Kaida, Shinnosuke; Kiawjak, Pornprom; Matsushima, Kousuke

doi:10.1109/icccs49078.2020.9118531

Cited by 2 publications

(1 citation statement)

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“…Despite the clear differences between these two pipeline structures, the ultimate objectives of the modern detectors being currently proposed are to accurately classify objects and estimate their locations in images. Although, concurrently, numerous different object detection frameworks have been proposed [ 13 , 14 , 15 ], designing a robust and accurate detection system is still a challenging task because of the various environmental problems encountered in real scenarios.…”

Section: Introductionmentioning

confidence: 99%

RGDiNet: Efficient Onboard Object Detection with Faster R-CNN for Air-to-Ground Surveillance

Kim¹,

Cho²

2021

Sensors

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An essential component for the autonomous flight or air-to-ground surveillance of a UAV is an object detection device. It must possess a high detection accuracy and requires real-time data processing to be employed for various tasks such as search and rescue, object tracking and disaster analysis. With the recent advancements in multimodal data-based object detection architectures, autonomous driving technology has significantly improved, and the latest algorithm has achieved an average precision of up to 96%. However, these remarkable advances may be unsuitable for the image processing of UAV aerial data directly onboard for object detection because of the following major problems: (1) Objects in aerial views generally have a smaller size than in an image and they are uneven and sparsely distributed throughout an image; (2) Objects are exposed to various environmental changes, such as occlusion and background interference; and (3) The payload weight of a UAV is limited. Thus, we propose employing a new real-time onboard object detection architecture, an RGB aerial image and a point cloud data (PCD) depth map image network (RGDiNet). A faster region-based convolutional neural network was used as the baseline detection network and an RGD, an integration of the RGB aerial image and the depth map reconstructed by the light detection and ranging PCD, was utilized as an input for computational efficiency. Performance tests and evaluation of the proposed RGDiNet were conducted under various operating conditions using hand-labeled aerial datasets. Consequently, it was shown that the proposed method has a superior performance for the detection of vehicles and pedestrians than conventional vision-based methods.

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Section: Introductionmentioning

confidence: 99%