A model for fine-grained vehicle classification based on deep learning

Yu, Shengsheng; Wu, Yun; Li, Wei; Song, Zhijian; Zeng, Wenhua

doi:10.1016/j.neucom.2016.09.116

Cited by 93 publications

(38 citation statements)

References 15 publications

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“…Faster R-CNN is adopted to recognise the location of tree trunks from every deep image and is composed of two CNNs, i.e., a region proposal network (RPN) that proposes regions and a fast region-based CNN (Fast R-CNN) detection network that uses the proposed regions. The RPN samples the information from a random region in the image as the proposal regions and trains them to determine the areas that may contain the target [36]. The Fast R-CNN detection network further processes the area information collected by the RPN network, determines the target category in the area, and precisely adjusts the size of this area to locate the specific location of the target in the image [26].…”

Section: Faster R-cnnmentioning

confidence: 99%

Individual Rubber Tree Segmentation Based on Ground-Based LiDAR Data and Faster R-CNN of Deep Learning

Wang

Chen

Cao

et al. 2019

Forests

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Rubber trees in southern China are often impacted by natural disturbances that can result in a tilted tree body. Accurate crown segmentation for individual rubber trees from scanned point clouds is an essential prerequisite for accurate tree parameter retrieval. In this paper, three plots of different rubber tree clones, PR107, CATAS 7-20-59, and CATAS 8-7-9, were taken as the study subjects. Through data collection using ground-based mobile light detection and ranging (LiDAR), a voxelisation method based on the scanned tree trunk data was proposed, and deep images (i.e., images normally used for deep learning) were generated through frontal and lateral projection transform of point clouds in each voxel with a length of 8 m and a width of 3 m. These images provided the training and testing samples for the faster region-based convolutional neural network (Faster R-CNN) of deep learning. Consequently, the Faster R-CNN combined with the generated training samples comprising 802 deep images with pre-marked trunk locations was trained to automatically recognize the trunk locations in the testing samples, which comprised 359 deep images. Finally, the point clouds for the lower parts of each trunk were extracted through back-projection transform from the recognized trunk locations in the testing samples and used as the seed points for the region’s growing algorithm to accomplish individual rubber tree crown segmentation. Compared with the visual inspection results, the recognition rate of our method reached 100% for the deep images of the testing samples when the images contained one or two trunks or the trunk information was slightly occluded by leaves. For the complicated cases, i.e., multiple trunks or overlapping trunks in one deep image or a trunk appearing in two adjacent deep images, the recognition accuracy of our method was greater than 90%. Our work represents a new method that combines a deep learning framework with point cloud processing for individual rubber tree crown segmentation based on ground-based mobile LiDAR scanned data.

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Section: Faster R-cnnmentioning

confidence: 99%

Individual Rubber Tree Segmentation Based on Ground-Based LiDAR Data and Faster R-CNN of Deep Learning

Wang

Chen

Cao

et al. 2019

Forests

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“…1) Task-level Parallel Training: In actual scenarios, multiple video analytics tasks are typically performed in a video file on a DIVS system. For example, many researchers applied different deep learning algorithms for traffic monitoring, such as CNN models for vehicle classification [5,24] and LSTM models for traffic flow prediction [25,26]. Therefore, we propose a task-level parallel training method for the distributed DL model.…”

Section: A Parallel Training Of Distributed DL Modelmentioning

confidence: 99%

Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing

Chen

Deng

et al. 2024

IEEE Trans. Ind. Inf.

171

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In this paper, we propose a Distributed Intelligent Video Surveillance (DIVS) system using Deep Learning (DL) algorithms and deploy it in an edge computing environment. We establish a multi-layer edge computing architecture and a distributed DL training model for the DIVS system. The DIVS system can migrate computing workloads from the network center to network edges to reduce huge network communication overhead and provide low-latency and accurate video analysis solutions. We implement the proposed DIVS system and address the problems of parallel training, model synchronization, and workload balancing. Task-level parallel and model-level parallel training methods are proposed to further accelerate the video analysis process. In addition, we propose a model parameter updating method to achieve model synchronization of the global DL model in a distributed EC environment. Moreover, a dynamic data migration approach is proposed to address the imbalance of workload and computational power of edge nodes. Experimental results showed that the EC architecture can provide elastic and scalable computing power, and the proposed DIVS system can efficiently handle video surveillance and analysis tasks.

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“…This network is named as feedforward because there are no feedback connections in which output of the model are fed back into itself. [20][21][22][23] Neural network was developed from single neuron named perceptron. It categorizes a set of input into one of two classes.…”

Section: Single Layer Perceptronmentioning

confidence: 99%

“…Yu et al developed a fine-grained vehicle classification approach which consisted of two parts: vehicle detection and classification model. 21 They employed Faster R-CNN method to extract single vehicle images from an image with clutter background. Then this single vehicle images were used to classify a vehicle by employing CNN with a joint Bayesian network.…”

Section: Introductionmentioning

confidence: 99%

Application of deep learning techniques in predicting motorcycle crash severity

Rezapour

Nazneen

Ksaibati

2020

Engineering Reports

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Machine learning (ML) techniques play a crucial role in today's modern world. Over the last years, road traffic safety is one of the applications where ML-methods have been successfully employed to prevent road users from being killed or seriously injured. A reliable data-driven predictive model is essential for this purpose. This could be achieved by successfully applying an intelligent transportation system to identify a driver at a higher risk of crashes. This study investigates the capabilities of different deep learning techniques to predict motorcycle crash severity. This study is based on 2,430 motorcycle crashes in a mountainous area in the United States over a 10-year period. Different deep networks (DNNs), including deep belief network, standard recurrent neural network (RNN), multilayer neural network, and single-layer neural network, were considered and compared in terms of prediction accuracy of motorcycle crash severity. Before conducting any analysis, feature reduction was performed to identify the optimal number of variables to include in the models by minimizing the error rate. Different metrics including the area under the curve and confusion matrix were used to compare the different models. Although the analyses were conducted on a relatively small dataset, the results indicate that almost all the DNN models better perform in predicting the severity of motorcycle crashes, compared with the single layer neural network. Finally, the RNN outperforms the other three neural network models. A comprehensive discussion has been made about the methodological approach implemented in this study.

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A model for fine-grained vehicle classification based on deep learning

Cited by 93 publications

References 15 publications

Individual Rubber Tree Segmentation Based on Ground-Based LiDAR Data and Faster R-CNN of Deep Learning

Individual Rubber Tree Segmentation Based on Ground-Based LiDAR Data and Faster R-CNN of Deep Learning

Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing

Application of deep learning techniques in predicting motorcycle crash severity

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