Image classification method is currently the more popular image technology, but it still has certain problems in practice. In order to improve the image classification effect, this study proposes a new convolution kernel, which can effectively detect the corresponding features with different transformations by actively transforming the relative positions of the connections in the convolution kernel. Moreover, in a network, replacing a traditional convolution kernel with a complex convolution kernel can significantly improve network performance. In order to verify the performance of the image classification method proposed in this study, the performance comparison of the algorithm was performed by setting a control experiment. The research results show that the proposed method has certain effects and can provide theoretical reference for subsequent related research. © 2020, Springer-Verlag London Ltd., part of Springer Nature.

In recent years, researchers have proposed many methods to solve the problem of obstacle detection. However, computer vision-based vehicle detection and recognition technology is still not mature enough. This research combines machine learning technology to construct a traffic object recognition system and applies innovative technology to the computer vision recognition system to construct an automatic identification system suitable for current traffic demand and improve the stability of the traffic system. Moreover, this study uses a combination of a monocular camera and a binocular camera to sense the traffic environment and obtain vehicle position and velocity information. In addition, this study is based on the binocular stereo camera to find the obstacle space and obtain the obstacle relative to the position and speed of the vehicle and combine the obstacle space information to optimize the obstacle frame of the target vehicle. Through experimental research and analysis, it can be seen that the algorithm proposed in this study has certain recognition effect and can be applied to traffic object recognition. © 2020, Springer-Verlag London Ltd., part of Springer Nature.

Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth's surface and has a wide range of applications in urban planning, environmental monitoring, agriculture investigation, disaster assessment, and map revision. In recent years, integrated artificial intelligence (AI) technology has become a research focus in developing new change detection methods. Although some researchers claim that AI-based change detection approaches outperform traditional change detection approaches, it is not immediately obvious how and to what extent AI can improve the performance of change detection. This review focuses on the state-of-the-art methods, applications, and challenges of AI for change detection. Specifically, the implementation process of AI-based change detection is first introduced. Then, the data from different sensors used for change detection, including optical RS data, synthetic aperture radar (SAR) data, street view images, and combined heterogeneous data, are presented, and the available open datasets are also listed. The general frameworks of AI-based change detection methods are reviewed and analyzed systematically, and the unsupervised schemes used in AI-based change detection are further analyzed. Subsequently, the commonly used networks in AI for change detection are described. From a practical point of view, the application domains of AI-based change detection methods are classified based on their applicability. Finally, the major challenges and prospects of AI for change detection are discussed and delineated, including (a) heterogeneous big data processing, (b) unsupervised AI, and (c) the reliability of AI. This review will be beneficial for researchers in understanding this field.

Today, blockchain uses a list of various blocks for storing a distributed flat of invariable information that informs of a set of replicated logical things in the Internet of Things (IoT). In the blockchain, a set of blocks includes various transactions containing a cryptographic hash value and a timestamp. Blockchain-as-a-service (BaaS) as a new service in cloud providers presents an infrastructure for accessing users to execute, manage, and monitor blockchain applications without high secured infrastructure requirements. Recently, BaaS concepts widely have used the management of IoT applications in different layers such as network, data, control, and resource. This paper provides a systematic review of recent research studies in BaaS models. The main goal behind this review is to categorize the applied scenarios, trends, evaluated Quality of Service (QoS) factors, new challenges, and open directions on BaaS models in IoT management. To evaluate the existing research studies in this field, five analytical research questions are proposed to analyze the technical aspects of each study. The analytical results based on existing research questions specify that the BaaS models are applied to network layer to manage IoT environment more than other layers. Also, security and privacy are two important factors to evaluate the existing BaaS models in cloud-edge IoT environments. Finally, the integration of BaaS models on IoT environments with interconnections of cloud-edge computing and software defined networks creates great secure opportunities for smart environment applications to monitor, manage, and improve all the atomic services and resources.