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A Hybrid Algorithm based on Deep Learning and Restricted Boltzmann Machine for Car Semantic Segmentation from Unmanned Aerial Vehicles (UAVs)-based Thermal Infrared Images
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Mahdi Khoshboresh Masouleh   , Reza Shah Hosseini * |
| University of Tehran |
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Abstract: (4204 Views) |
| Nowadays, ground vehicle monitoring (GVM) is one of the areas of application in the intelligent traffic control system using image processing methods. In this context, the use of unmanned aerial vehicles based on thermal infrared (UAV-TIR) images is one of the optimal options for GVM due to the suitable spatial resolution, cost-effective and low volume of images. The methods that have been proposed for vehicle extraction from thermal infrared imaging often experience problems such as low accuracy in detection, segmentation (e.g. HOG+SVM) and also the need for big data training (e.g. deep learning methods). In the present study, a new model, called SegRBM-Net, based on deep learning (DL) and the restricted Boltzmann machine (RBM) is being presented. One of the features of the SegRBM-Net model is the improving accuracy of vehicle detection and segmentation from thermal infrared images by using both convolutional layers and the features of the Gaussian-Bernoulli restricted Boltzmann machine. This structure has led the algorithm to find the target faster and more accurately than other DL methods. To examine the performance of the proposed method, we performed a controlled benchmark (e.g. high density of vehicles scene, and difference in viewing angle) of SegRBM-Net and other DL models on four UAV-TIR image datasets.The results showed that the SegRBM-Net model with a mean accuracy of 99% and improved processing speed compared with similar methods have a good performance. |
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| Keywords: Convolutional Neural Networks, Gaussian-Bernoulli Restricted Boltzmann Machine, Semantic Segmentation, Ground Vehicle, Thermal Infrared UAV Imagery |
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Full-Text [PDF 2174 kb]
(2282 Downloads)
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Type of Study: Research |
Subject:
RS
Received: 2018/12/15 | Accepted: 2019/06/15 | Published: 2020/03/19
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