With the rapid development of object detection technology for unmanned aerial vehicles (UAVs), it is convenient to collect data from UAV aerial photographs. They have a wide range of applications in several fields, such as monitoring, geological exploration, precision agriculture, and disaster early warning. In recent years, many methods based on artificial intelligence have been proposed for UAV object detection, and deep learning is a key area in this field. Significant progress has been achieved in the area of deep-learning-based UAV object detection. Thus, this paper presents a review of recent research on deep-learning-based UAV object detection. This survey provides an overview of the development of UAVs and summarizes the deep-learning-based methods in object detection for UAVs. In addition, the key issues in UAV object detection are analyzed, such as small object detection, object detection under complex backgrounds, object rotation, scale change, and category imbalance problems. Then, some representative solutions based on deep learning for these issues are summarized. Finally, future research directions in the field of UAV object detection are discussed.

A self-driving car is a hot research topic in the field of the intelligent transportation system, which can greatly alleviate traffic jams and improve travel efficiency. Scene classification is one of the key technologies of self-driving cars, which can provide the basis for decision-making in self-driving cars. In recent years, deep learning-based solutions have achieved good results in the problem of scene classification. However, some problems should be further studied in the scene classification methods, such as how to deal with the similarities among different categories and the differences among the same category. To deal with these problems, an improved deep network-based scene classification method is proposed in this article. In the proposed method, an improved faster region with convolutional neural network features (RCNN) network is used to extract the features of representative objects in the scene to obtain local features, where a new residual attention block is added to the Faster RCNN network to highlight local semantics related to driving scenarios. In addition, an improved Inception module is used to extract global features, where a mixed Leaky ReLU and ELU function is presented, to reduce the possible redundancy of the convolution kernel and enhance the robustness. Then, the local features and the global features are fused to realize the scene classification. Finally, a private dataset is built from the public datasets for the specialized application of scene classification in the self-driving field, and the proposed method is tested on the proposed dataset. The experimental results show that the accuracy of the proposed method can reach 94.76%, which is higher than the state-of-the-art methods.

The advent of Question Answering Systems (QASs) has been envisaged as a promising solution and an efficient approach for retrieving significant information over the Internet. A considerable amount of research work has focused on open domain QASs based on deep learning techniques due to the availability of data sources. However, the medical domain receives less attention due to the shortage of medical datasets. Although Electronic Health Records (EHRs) are empowering the field of Medical Question-Answering (MQA) by providing medical information to answer user questions, the gap is still large in the medical domain, especially for textual-based sources. Therefore, in this study, the medical textual question-answering systems based on deep learning approaches were reviewed, and recent architectures of MQA systems were thoroughly explored. Furthermore, an in-depth analysis of deep learning approaches used in different MQA system tasks was provided. Finally, the different critical challenges posed by MQA systems were highlighted, and recommendations to effectively address them in forthcoming MQA systems were given out.

Unmanned Aerial Vehicle (UAV) has been widely used in a variety of application, and the target search is one of the hot issues in the UAV research fields. Compared with the single UAV, the multi-UAV system can be competent for more complex tasks, with higher execution efficiency and stronger robustness. However, there exist some new challenges in the multi-UAV cooperative search, such as collaborative control and search area covering problems. To complete these tasks efficiently, the cooperative search problem is modeled as a potential game, and a modified binary log linear learning (BLLL) algorithm is proposed in this paper, to solve the covering problem using multiple UAVs. Furthermore, to improve the cooperative control performance based on potential game theory, a novel action selection strategy for UAVs is proposed. This strategy can avoid a UAV wandering around at the zero utility area by exchanging the information with neighbors. Finally, various simulations are carried out. The experimental results show that the proposed method can effectively complete cooperative search tasks and has better performance than the original BLLL algorithm.

Support vector regression (SVR) is one of the most powerful and widely used machine learning algorithms regarding prediction. The kernel type, penalty factor and other parameters influence the efficiency and performance of SVR deeply. The optimization of these parameters is held a hot issue. In this work, we propose a SVR based prediction approach using henry gas solubility optimization algorithm (HGSO), which is a recent meta-heuristic algorithm inspired by Henry’s law. First, SVR parameters are randomly generated in some certain ranges to form parameter population. Second, the prediction accuracies (PAs) are obtained using the population and SVR. Thirdly, the population and optimal SVR parameters are updated via PAs and HGSO. We repeat the second and third steps until the cut-off conditions are met. Ten low-and high-dimensional benchmark data sets are utilized to assess the prediction accuracy, convergence performance and computational complexity of the presented approach and other well-known algorithms. The experimental results reveal that our approach has the optimum comprehensive performance.

The low-light image enhancement is a challenging and hot research issue in the image processing field. In order to enhance the quality of low-light images to obtain full structure and details, many low-light image enhancement algorithms have been proposed and deep learning-based methods have achieved great success in this field. However, most of the deep learning methods require paired training data, which is difficult to obtain. And the overall visual quality of the enhanced image is still not very satisfying. To deal with these problems, an unsupervised low-light image enhancement model based on an improved Cycle-Consistent Generative Adversarial Networks (CycleGAN) is proposed in this paper. In the proposed model, a low-light enhancement generator of the CycleGAN network is constructed based on an improved U-Net structure, and the adaptive instance normalization (AdaIN) is designed to learn the style of the normal light image. In particular, a detail enhancement method based on multi-layer guided filtering is added to the proposed model, which can improve the quality and visual pleasantness of image enhancement. In addition, a joint training strategy based on structural similarity is presented, to strengthen the constraints on generating more realistic and natural images. At last, extensive experiments are conducted and the results show that the proposed method can accomplish the task of transferring low-light images to normal light and outperform the state-of-the-art approaches in various metrics of visual quality.