An intelligent transportation system (ITS) plays an important role in public transport management, security and other issues. Traffic flow detection is an important part of the ITS. Based on the real-time acquisition of urban road traffic flow information, an ITS provides intelligent guidance for relieving traffic jams and reducing environmental pollution. The traffic flow detection in an ITS usually adopts the cloud computing mode. The edge of the network will transmit all the captured video to the cloud computing center. However, the increasing traffic monitoring has brought great challenges to the storage, communication and processing of traditional transportation systems based on cloud computing. To address this issue, a traffic flow detection scheme based on deep learning on the edge node is proposed in this article. First, we propose a vehicle detection algorithm based on the YOLOv3 (You Only Look Once) model trained with a great volume of traffic data. We pruned the model to ensure its efficiency on the edge equipment. After that, the DeepSORT (Deep Simple Online and Realtime Tracking) algorithm is optimized by retraining the feature extractor for multiobject vehicle tracking. Then, we propose a real-time vehicle tracking counter for vehicles that combines the vehicle detection and vehicle tracking algorithms to realize the detection of traffic flow. Finally, the vehicle detection network and multiple-object tracking network are migrated and deployed on the edge device Jetson TX2 platform, and we verify the correctness and efficiency of our framework. The test results indicate that our model can efficiently detect the traffic flow with an average processing speed of 37.9 FPS (frames per second) and an average accuracy of 92.0% on the edge device.

Image segmentation based on superpixel is used in urban and land cover change detection for fast locating region of interest. However, the segmentation algorithms often degrade due to speckle noise in synthetic aperture radar images. In this paper, a feature learning method using stacked contractive autoencoder is presented to extract temporal change feature from superpixel with noise suppression. Firstly, an affiliated temporal change image which obtains temporal difference in pixel level are built by three different metrics. Secondly, the simple linear iterative clustering algorithm is used to generate superpixels which tightly adhere to the change image boundaries for the purpose of acquiring homogeneous change samples. Thirdly, a stacked Contractive autoencoder (sCAE) network are trained with the superpixel samples as input to learn the change features in semantic. And then, the encoded features by this sCAE model are binary classified to create the change result map. Finally, the proposed method is compared with methods based on PCA and MRF. Experiment results show our deep learning model can separate non-linear noise efficiently from change features and obtain better performance in change detection for SAR images than conventional change detection algorithms.

Edge intelligence (EI) migrates data and artificial intelligence (AI) to the “edge” of a network, enhancing the high-bandwidth and low-latency of wireless data transmission with the multiplier effect of 5G and AI, greatly improving the edges’ processing speed. Through integrating EI and computer vision technology, video surveillance systems in ITS can improve the processing capability of traffic information, which improves traffic efficiency and ensures traffic safety. Accordingly, first, we propose an edge intelligence-based improved-YOLOv4 vehicle detection algorithm, introducing an efficient channel attention (ECA) mechanism and a high-resolution network (HRNet) to enhance vehicle detection ability. Second, an edge intelligence-based improved DeepLabv3+ image segmentation algorithm is proposed, replacing the original backbone network with MobileNetv2 and using the softpool method, thus reducing the network size while improving the segmentation accuracy. Experimental results show that our proposed model has a higher average precision (AP) and can improve vehicle detection accuracy from 82.03% to 86.22%. The mean intersection over union (mIOU) of the image segmentation model improves from 73.32% to 75.63%.

Mobile Edge Computing (MEC) plays a significant role in enabling diverse service applications by implementing efficient data sharing. However, the unique characteristics of MEC also bring data privacy and security problem, which impedes the development of MEC. Blockchain is viewed as a promising technology to guarantee the security and traceability of data sharing. Nonetheless, how to integrate blockchain into MEC system is quite challenging because of dynamic characteristics of channel conditions and network loads. To this end, we propose a secure data sharing scheme in blockchain-enabled MEC system using an asynchronous learning approach in this paper. First, a blockchain-enabled secure data sharing framework in MEC system is presented. Then, we present an adaptive privacy-preserving mechanism according to available system resources and privacy demands of users. Next, an optimization problem of secure data sharing is formulated in blockchain-enabled MEC system with the aim to maximize the system performance with respect to the decreased energy consumption of MEC system and the increased throughput of blockchain system. Specially, an asynchronous learning approach is employed to solve the formulated problem. Numerical results demonstrate the superiority of our proposed secure data sharing scheme when compared with some popular benchmark algorithms in terms of average throughput, average energy consumption and reward.