RGDiNet: Efficient Onboard Object Detection with Faster R-CNN for Air-to-Ground Surveillance
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. W...
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| Format: | Article |
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MDPI AG
2021-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/5/1677 |
| _version_ | 1797416681464659968 |
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| author | Jongwon Kim Jeongho Cho |
| author_facet | Jongwon Kim Jeongho Cho |
| author_sort | Jongwon Kim |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T06:06:41Z |
| format | Article |
| id | doaj.art-ce12b1f7051446c38d23e4f03bb7fd6b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T06:06:41Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ce12b1f7051446c38d23e4f03bb7fd6b2023-12-03T12:02:48ZengMDPI AGSensors1424-82202021-03-01215167710.3390/s21051677RGDiNet: Efficient Onboard Object Detection with Faster R-CNN for Air-to-Ground SurveillanceJongwon Kim0Jeongho Cho1Department of Electrical Engineering, Soonchunhyang University, Asan 31538, KoreaDepartment of Electrical Engineering, Soonchunhyang University, Asan 31538, KoreaAn 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.https://www.mdpi.com/1424-8220/21/5/1677onboard detectionUAVfaster R-CNNair-to-ground surveillance |
| spellingShingle | Jongwon Kim Jeongho Cho RGDiNet: Efficient Onboard Object Detection with Faster R-CNN for Air-to-Ground Surveillance Sensors onboard detection UAV faster R-CNN air-to-ground surveillance |
| title | RGDiNet: Efficient Onboard Object Detection with Faster R-CNN for Air-to-Ground Surveillance |
| title_full | RGDiNet: Efficient Onboard Object Detection with Faster R-CNN for Air-to-Ground Surveillance |
| title_fullStr | RGDiNet: Efficient Onboard Object Detection with Faster R-CNN for Air-to-Ground Surveillance |
| title_full_unstemmed | RGDiNet: Efficient Onboard Object Detection with Faster R-CNN for Air-to-Ground Surveillance |
| title_short | RGDiNet: Efficient Onboard Object Detection with Faster R-CNN for Air-to-Ground Surveillance |
| title_sort | rgdinet efficient onboard object detection with faster r cnn for air to ground surveillance |
| topic | onboard detection UAV faster R-CNN air-to-ground surveillance |
| url | https://www.mdpi.com/1424-8220/21/5/1677 |
| work_keys_str_mv | AT jongwonkim rgdinetefficientonboardobjectdetectionwithfasterrcnnforairtogroundsurveillance AT jeonghocho rgdinetefficientonboardobjectdetectionwithfasterrcnnforairtogroundsurveillance |