Vehicle detection from high-resolution aerial images using spatial pyramid pooling-based deep convolutional neural networks

Qu, Tao; Zhang, Quanyuan; Sun, Shilei

doi:10.1007/s11042-016-4043-5

Cited by 66 publications

(32 citation statements)

References 19 publications

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“…Compared with hand-crafted features or shallow learning features, deep CNN features are more powerful in object representation. Therefore, some methods [11,30,31] replaced hand-crafted features in traditional methods with deep CNN features. This strategy can significantly improve the performance of object detector, but is still time consuming.…”

Section: Vehicle Detection Methods For Aerial Imagesmentioning

confidence: 99%

Arbitrary-Oriented Vehicle Detection in Aerial Imagery with Single Convolutional Neural Networks

et al. 2017

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Vehicle detection with orientation estimation in aerial images has received widespread interest as it is important for intelligent traffic management. This is a challenging task, not only because of the complex background and relatively small size of the target, but also the various orientations of vehicles in aerial images captured from the top view. The existing methods for oriented vehicle detection need several post-processing steps to generate final detection results with orientation, which are not efficient enough. Moreover, they can only get discrete orientation information for each target. In this paper, we present an end-to-end single convolutional neural network to generate arbitrarily-oriented detection results directly. Our approach, named Oriented_SSD (Single Shot MultiBox Detector, SSD), uses a set of default boxes with various scales on each feature map location to produce detection bounding boxes. Meanwhile, offsets are predicted for each default box to better match the object shape, which contain the angle parameter for oriented bounding boxes' generation. Evaluation results on the public DLR Vehicle Aerial dataset and Vehicle Detection in Aerial Imagery (VEDAI) dataset demonstrate that our method can detect both the location and orientation of the vehicle with high accuracy and fast speed. For test images in the DLR Vehicle Aerial dataset with a size of 5616 × 3744, our method achieves 76.1% average precision (AP) and 78.7% correct direction classification at 5.17 s on an NVIDIA GTX-1060.

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Section: Vehicle Detection Methods For Aerial Imagesmentioning

confidence: 99%

Arbitrary-Oriented Vehicle Detection in Aerial Imagery with Single Convolutional Neural Networks

et al. 2017

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“…The detection of objects in aerial images has been extensively studied over the last few decades, although most works [46][47][48][49][50][51][52][53][54][55][56] focused on extracting the discriminative feature and generating accurate region proposals. Xu et al [46] employed the Viola-Jones approach, the histogram of oriented gradients (HOG) features and the linear support vector machine (SVM) to design a model for vehicle detection in UAV imagery, which exhibited a high performance.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, CNNs have become the most prominent technique for object detection in aerial images, and most of the related approaches are developed based on two schemes. In some cases, the CNN is applied to replace the traditional hand-crafted features for feature extraction [47,48]. Ammour et al [47] adopted a CNN as a feature extractor to detect objects in UAV images; they first employed a segmentation approach to generate the candidate regions from the input image and later fed the regions into a CNN model (which was pre-trained) for feature extraction.…”

Section: Related Workmentioning

confidence: 99%

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Multiple-Oriented and Small Object Detection with Convolutional Neural Networks for Aerial Image

2019

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Detecting objects in aerial images is a challenging task due to multiple orientations and relatively small size of the objects. Although many traditional detection models have demonstrated an acceptable performance by using the imagery pyramid and multiple templates in a sliding-window manner, such techniques are inefficient and costly. Recently, convolutional neural networks (CNNs) have successfully been used for object detection, and they have demonstrated considerably superior performance than that of traditional detection methods; however, this success has not been expanded to aerial images. To overcome such problems, we propose a detection model based on two CNNs. One of the CNNs is designed to propose many object-like regions that are generated from the feature maps of multi scales and hierarchies with the orientation information. Based on such a design, the positioning of small size objects becomes more accurate, and the generated regions with orientation information are more suitable for the objects arranged with arbitrary orientations. Furthermore, another CNN is designed for object recognition; it first extracts the features of each generated region and subsequently makes the final decisions. The results of the extensive experiments performed on the vehicle detection in aerial imagery (VEDAI) and overhead imagery research data set (OIRDS) datasets indicate that the proposed model performs well in terms of not only the detection accuracy but also the detection speed.

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“…One of the proposed solutions is the implementation of a deep learning-based software which uses a convolutional neural network algorithm to track, detect, and classify objects from raw data in real time. In the last few years, deep convolutional neural networks have shown to be a reliable approach for image object detection and classification due to their relatively high accuracy and speed [6][7][8][9]. Furthermore, a CNN algorithm enables UAVs to convert object information from the immediate environment into abstract information that can be interpreted by machines without human interference.…”

Section: Introductionmentioning

confidence: 99%

Object Recognition in Aerial Images Using Convolutional Neural Networks

2017

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Abstract:There are numerous applications of unmanned aerial vehicles (UAVs) in the management of civil infrastructure assets. A few examples include routine bridge inspections, disaster management, power line surveillance and traffic surveying. As UAV applications become widespread, increased levels of autonomy and independent decision-making are necessary to improve the safety, efficiency, and accuracy of the devices. This paper details the procedure and parameters used for the training of convolutional neural networks (CNNs) on a set of aerial images for efficient and automated object recognition. Potential application areas in the transportation field are also highlighted. The accuracy and reliability of CNNs depend on the network's training and the selection of operational parameters. This paper details the CNN training procedure and parameter selection. The object recognition results show that by selecting a proper set of parameters, a CNN can detect and classify objects with a high level of accuracy (97.5%) and computational efficiency. Furthermore, using a convolutional neural network implemented in the "YOLO" ("You Only Look Once") platform, objects can be tracked, detected ("seen"), and classified ("comprehended") from video feeds supplied by UAVs in real-time.

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Vehicle detection from high-resolution aerial images using spatial pyramid pooling-based deep convolutional neural networks

Cited by 66 publications

References 19 publications

Arbitrary-Oriented Vehicle Detection in Aerial Imagery with Single Convolutional Neural Networks

Arbitrary-Oriented Vehicle Detection in Aerial Imagery with Single Convolutional Neural Networks

Multiple-Oriented and Small Object Detection with Convolutional Neural Networks for Aerial Image

Object Recognition in Aerial Images Using Convolutional Neural Networks

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