Convolutional Neural Networks for Aerial Vehicle Detection and Recognition

Vehicle detection and recognition require demanding advanced computational intelligence and resources in a real-time traffic surveillance system for effective traffic management of all possible contingencies. One of the focus areas of deep intelligent systems is to facilitate vehicle detection and recognition techniques for robust traffic management of heavy vehicles. The following are such sophisticated mechanisms: Support Vector Machine (SVM), Convolutional Neural Networks (CNN), Regional Convolutional Neural Networks (R-CNN), You Only Look Once (YOLO) model, etcetera. Accordingly, it is pivotal to choose the precise algorithm for vehicle detection and recognition, which also addresses the real-time environment. In this study, a comparison of deep learning algorithms, such as the Faster R-CNN, YOLOv2, YOLOv3, and YOLOv4, are focused on diverse aspects of the features. Two entities for transport heavy vehicles, the buses and trucks, constitute detection and recognition elements in this proposed work. The mechanics of data augmentation and transfer-learning is implemented in the model; to build, execute, train, and test for detection and recognition to avoid over-fitting and improve speed and accuracy. Extensive empirical evaluation is conducted on two standard datasets such as COCO and PASCAL VOC 2007. Finally, comparative results and analyses are presented based on real-time.

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Marker-Less Micro Aerial Vehicle Detection and Localization Using Convolutional Neural Networks

IEEE Robotics and Automation Letters ◽

10.1109/lra.2020.2972819 ◽

2020 ◽

Vol 5 (2) ◽

pp. 2459-2466 ◽

Cited By ~ 3

Author(s):

Matous Vrba ◽

Martin Saska

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Vehicle Detection ◽

Micro Aerial Vehicle ◽

Aerial Vehicle ◽

Detection And Localization

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Fast Automatic Vehicle Detection in UAV Images Using Convolutional Neural Networks

Remote Sensing ◽

10.3390/rs12121994 ◽

2020 ◽

Vol 12 (12) ◽

pp. 1994 ◽

Cited By ~ 1

Author(s):

Xin Luo ◽

Xiaoyue Tian ◽

Huijie Zhang ◽

Weimin Hou ◽

Geng Leng ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Vehicle Detection ◽

Neural Computing ◽

Detection Capability ◽

High Processing Speed ◽

Aerial Vehicle ◽

Uav Images ◽

Uav Image ◽

High Processing

Vehicle targets in unmanned aerial vehicle (UAV) images are generally small, so a significant amount of detailed information on targets may be lost after neural computing, which leads to the poor performances of the existing recognition algorithms. Based on convolutional neural networks that utilize the YOLOv3 algorithm, this article focuses on the development of a quick automatic vehicle detection method for UAV images. First, a vehicle dataset for target recognition is constructed. Then, a novel YOLOv3 vehicle detection framework is proposed according to the following characteristics: The vehicle targets in the UAV image are relatively small and dense. The average precision (AP) increased by 5.48%, from 92.01% to 97.49%, which still remains the rather high processing speed of the YOLO network. Finally, the proposed framework is tested using three datasets: COWC, VEDAI, and CAR. The experimental results demonstrate that our method had a better detection capability.

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Towards Amazon Forest Restoration: Automatic Detection of Species from UAV Imagery

Remote Sensing ◽

10.3390/rs13132627 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2627

Author(s):

Marks Melo Moura ◽

Luiz Eduardo Soares de Oliveira ◽

Carlos Roberto Sanquetta ◽

Alexis Bastos ◽

Midhun Mohan ◽

...

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Forest Restoration ◽

Amazon Forest ◽

Forest Species ◽

Accuracy And Precision ◽

Identification Of Species ◽

Aerial Vehicle ◽

High Resolution Images ◽

Uav Images

Precise assessments of forest species’ composition help analyze biodiversity patterns, estimate wood stocks, and improve carbon stock estimates. Therefore, the objective of this work was to evaluate the use of high-resolution images obtained from Unmanned Aerial Vehicle (UAV) for the identification of forest species in areas of forest regeneration in the Amazon. For this purpose, convolutional neural networks (CNN) were trained using the Keras–Tensorflow package with the faster_rcnn_inception_v2_pets model. Samples of six forest species were used to train CNN. From these, attempts were made with the number of thresholds, which is the cutoff value of the function; any value below this output is considered 0, and values above are treated as an output 1; that is, values above the value stipulated in the Threshold are considered as identified species. The results showed that the reduction in the threshold decreases the accuracy of identification, as well as the overlap of the polygons of species identification. However, in comparison with the data collected in the field, it was observed that there exists a high correlation between the trees identified by the CNN and those observed in the plots. The statistical metrics used to validate the classification results showed that CNN are able to identify species with accuracy above 90%. Based on our results, which demonstrate good accuracy and precision in the identification of species, we conclude that convolutional neural networks are an effective tool in classifying objects from UAV images.

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