Objects in unmanned aerial vehicle (UAV) images are generally small due to the high photography altitude. Although many efforts have been made in object detection, how to accurately and quickly detect small objects is still one of the remaining open challenges. In this paper, we propose a feature fusion and scaling-based single shot detector (FS-SSD) for small object detection in UAV images. FS-SSD is an enhancement based on FSSD, a variety of the original single shot multibox detector (SSD). We add an extra scaling branch of the deconvolution module with an average pooling operation to form a feature pyramid. The original feature fusion branch is adjusted to be better suited to the small object detection task. The two feature pyramids generated by the deconvolution module and feature fusion module are utilized to make predictions together. In addition to the deep features learned by FS-SSD, to further improve the detection accuracy, spatial context analysis is proposed to incorporate the object spatial relationships into object redetection. The interclass and intraclass distances between different object instances are computed as spatial context, which proves effective for multiclass small object detection. Six experiments are conducted on the PASCAL VOC dataset and two UAV image datasets. The experimental results demonstrate that the proposed method can achieve a comparable detection speed but an accuracy superior to those of the six state-of-the-art methods.

In this paper, we consider an unmanned aerial vehicle (UAV) equipped with an air-to-ground friendly jammer assisting secure communications between a legitimate transmitter-receiver pair for unknown eavesdropper location. To enhance the reliability and security of this system, we examine the impact of the UAV jamming power and its three-dimensional (3D) spatial deployment on the outage probability (OP) of the legitimate receiver and the intercept probability (IP) of the eavesdropper. Based on the IP, we develop a new security measure, which we refer to as the intercept probability security region (IPSR). The IPSR defines the region within a target area where the IP is less than a given threshold. We design a low-complexity iterative algorithm to maximize the IPSR subject to an OP constraint by jointly optimizing the 3D deployment and jamming power of the UAV jammer. Numerical results show that our proposed iterative algorithm performs close to an exhaustive search with significantly reduced complexity.

Object tracking in RGB-thermal (RGB-T) videos is increasingly used in many fields due to the all-weather and all-day working capability of the dual-modality imaging system, as well as the rapid development of low-cost and miniaturized infrared camera technology. However, it is still very challenging to effectively fuse dual-modality information to build a robust RGB-T tracker. In this paper, an RGB-T object tracking algorithm based on a modal-aware attention network and competitive learning (MaCNet) is proposed, which includes a feature extraction network, modal-aware attention network, and classification network. The feature extraction network adopts the form of a two-stream network to extract features from each modality image. The modal-aware attention network integrates the original data, establishes an attention model that characterizes the importance of different feature layers, and then guides the feature fusion to enhance the information interaction between modalities. The classification network constructs a modality-egoistic loss function through three parallel binary classifiers acting on the RGB branch, the thermal infrared branch, and the fusion branch, respectively. Guided by the training strategy of competitive learning, the entire network is fine-tuned in the direction of the optimal fusion of the dual modalities. Extensive experiments on several publicly available RGB-T datasets show that our tracker has superior performance compared to other latest RGB-T and RGB tracking approaches.

Captured images of outdoor scenes usually exhibit low visibility in cases of severe haze, which interferes with optical imaging and degrades image quality. Most of the existing methods solve the single-image dehazing problem by applying supervised training on paired images; however, in practice, the pairing of real-world images is not viable. Additionally, the processing speed of individual dehazing models is important in practical applications. In this study, a novel unsupervised single image dehazing network (USID-Net) based on disentangled representations without paired training images is explored. Furthermore, considering the trade-off between performance and memory storage, a compact multi-scale feature attention (MFA) module is developed, integrating multi-scale feature representation and attention mechanism to facilitate feature representation. To effectively extract haze information, a mechanism referred to as OctEncoder is designed to include multi-frequency representations that can capture more global information. Extensive experiments show that USID-Net achieves competitive dehazing results and a relatively high processing speed compared to state-of-the-art methods.

Mobile edge computing (MEC) is a promising approach that can reduce the latency of task processing by offloading tasks from user equipments (UEs) to MEC servers. Existing works always assume that the MEC server is capable of executing the offloaded tasks, without considering the impact of improper load on task processing efficiency. In this article, we present a two-stage computing offloading scheme to minimize the task processing delay while managing the server load properly. To minimize the task processing delay, each UE optimizes how much workload to be offloaded to the MEC server. To improve the task processing efficiency of the server, we arrange the processing order of offloading tasks by introducing an aggregative game with an instantaneous load billing mechanism. The proposed game can obtain the optimal task offloading and processing strategy with limited information and a small number of iterations. Simulation results show that our scheme approaches the optimal offloading strategy in terms of minimizing task processing delay for each UE and improving processing efficiency for the server.