Deep neural networks (DNNs) have become essential for aerial detection. However, DNNs are vulnerable to adversarial examples, which pose great security concerns for security-critical systems. Researchers recently devised adversarial patches to evaluate the vulnerability of DNNs-based aerial detection methods physically. Nonetheless, adversarial patches generated by existing algorithms are not strong enough and extremely time-consuming. Moreover, the complicated physical factors are not accommodated well during the optimization process. In this paper, a novel adaptive-patch-based physical attack (AP-PA) framework is proposed to alleviate the above problems, which achieves state-of-the-art performance in both accuracy and efficiency. Specifically, the AP-PA aims to generate adversarial patches that are adaptive in both physical dynamics and varying scales, and by which the particular targets can be hidden from being detected. Furthermore, the adversarial patch is also gifted with attack effectiveness against all targets of the same class with a patch outside the target (No need to smear targeted objects) and robust enough in the physical world. In addition, a new loss is devised to consider more available information of detected objects to optimize the adversarial patch, which can significantly improve the patch’s attack efficacy (Average precision drop up to 87.86% and 85.48% in white-box and black-box settings, respectively) and optimizing efficiency. We also establish one of the first comprehensive, coherent, and rigorous benchmarks to evaluate the attack efficacy of adversarial patches on aerial detection tasks. Finally, several proportionally scaled experiments are performed physically to demonstrate that the elaborated adversarial patches can successfully deceive aerial detection algorithms in dynamic physical circumstances. The code is available at https://github.com/JiaweiLian/AP-PA.

Aerial visible-to-infrared image translation aims to transfer aerial visible images to their corresponding infrared images, which can effectively generate the infrared images of specific targets. Although some image-to-image translation algorithms have been applied to color-to-thermal natural images and achieved impressive results, they cannot be directly applied to aerial visible-to-infrared image translation due to the substantial differences between natural images and aerial images, including shooting angles, multi-scale targets, and complicated backgrounds. In order to verify the performance of existing image-to-image translation algorithms on aerial scenes as well as advance the development of aerial visible-to-infrared image translation, an Aerial Visible-to-Infrared Image Dataset (AVIID) is created, which is the first specialized dataset for aerial visible-to-infrared image translation and consists of over 3,000 paired visible-infrared images. Over the constructed AVIID, a complete evaluation system is presented to evaluate the generated infrared images from 2 aspects: overall appearance and target quality. In addition, a comprehensive survey of existing image-to-image translation approaches that could be applied to aerial visible-to-infrared image translation is given. We then provide a performance analysis of a set of representative methods under our proposed evaluation system on AVIID, which can serve as baseline results for future work. Finally, we summarize some meaningful conclusions, problems of existing methods, and future research directions to advance state-of-the-art algorithms for aerial visible-to-infrared image translation.

Remote sensing image dehazing is of great scientific interest and application value in both military and civil fields. In this article, we propose an enhanced attention-guide generative adversarial network (GAN) network, Dehaze-AGGAN, to solve the remote sensing images dehazing problem, which does not require paired training data. Since haze images have a great influence on remote sensing object detection, the dehazing of remote sensing images has become significantly important. Typical image dehazing methods require a hazy input image and its ground truth in a paired manner, while paired training data are usually not available in the field of remote sensing. To solve this problem, we propose the Dehaze-AGGAN network and train it by feeding unpaired clean and hazy images into the model. We present a novel total variation loss combined with the cycle consistency loss to eliminate wave noise and improve the target edge quality in the test dataset. Moreover, we present a new dehazing dataset called remote sensing dehazing dataset (RSD), which contains 7000 simulate and real hazy images including 3500 warship images and 3500 civilian ship images, and evaluate our method in the dataset. We conduct experiments on RSD. Extensive experiments demonstrate that the proposed Dehaze-AGGAN is effective and has strong robustness and adaptability in different settings.

Aerial Person Detection (APD) is vital for enhancing search and rescue (SaR) operations, particularly when locating victims in remote, poorly-lit areas. Despite advancements in detection technologies, achieving a balance between detection speed and accuracy on mobile devices in “edge AI” continues to pose challenges. In this paper, a lightweight distillation Network (APDNet) proposed for edge deployment of APD, which enable real-time inference as well as minimize accuracy loss during model transfer. The proposed APDNet employs a distillation network between varying-depth backbones and integrates an 8-bit quantized optimizer to reduce the floating-point operations of network parameters. Specifically, in the Teach-Assistant Distillation stage, small student models using random weight initialization are trained with pseudo-labels generated by deeper teacher models, facilitating consistent learning for a more accurate, lighter model. Moreover, a Low-Precision Quantization (LPQ) stage incorporates an offline, quantization-aware training strategy that dynamically adjusts the ranges of weight and activation function float-point values, reducing computational complexity. In order to compensate for the potential accuracy decline, a pluggable tracker updates the position and feature information of persons frame-by-frame, with tracking results integrated with detection outputs to enhance accuracy. Extensive experiments on the Heridal, Manipal-UAV, and VTSaR datasets confirm the effectiveness of APDNet, demonstrating its superior performance in edge-based aerial person detection.

Finding tiny persons under the drone vision was, is, and remains to be an integral and challenging task. Unmanned aerial vehicles (UAVs) with high-speed, low-altitude, and multi-perspective flight bring about violently various scales of objects, which burdens the optimization of models. Moreover, the detection performance of densely and faintly discernible person characteristics is far less than that of large objects in high-resolution aerial images. In this article, we introduce the image cropping strategy and attention mechanism based on YOLOv5 to address small person detection in the optimized VisDrone2019 dataset. Specifically, we propose a Densely Cropped and Local Attention of object detector Network (DCLANet), which is inspired by the observation that less area occupied by small objects should be fully focused and relatively magnified in the original image. DCLANet-assembled Density Map-Guided Object Detection (DMNet) in aerial images and You Only Look Twice (YOLT): Rapid Multiscale Object Detection In Satellite Imagery to crop images upon training and testing stage, meanwhile, added bottleneck attention mechanism to YOLOv5 baseline framework, which more focus on person objects other than irrelevant categories. To achieve further improvement of DCLANet, we also provide bags of useful strategies: data augmentation, label fusion, category filtering, and hyperparameter evolution. Extensive experiments on the VisDrone2019 show that DCLANet achieves state-of-the-art performanc; the detection result of person category $A P^{\text{val }}$ @0.5 is 50.04% with test-dev subset, which is substantially better than the previous SOTA method (DPNetV3) by 12.01%. In addition, on our optimized VisDrone2019 dataset, $A P^{\text{val }}$ @0.5 and $A P^{\text{test }}$ @0.5 obtained 74.95% and 62.18%, respectively. Compared to YOLOv5, DCLANet improves 3.8% or so, which is encouraging and competitive.

Image-to-image (I2I) translation methods based on Generative Adversarial Networks (GANs) have shown general solutions for aerial visible-to-infrared image translation (AVIT) task. Though existing approaches have made impressive results, they still struggle to produce diverse or high-realism translated aerial infrared images (AIIs). In this paper, a novel model is proposed to achieve both diverse and realistic AVIT, named DR-AVIT. Specifically, we introduce disentangled representation learning to disentangle the image representation of aerial visible images (AVIs) and AIIs into a domain-invariant semantic structure space and two domain-specific imaging style spaces. By leveraging this disentanglement, our model can perform the translation process conditioned on semantic structure information derived from the input AVI and randomly sampled imaging style features from the AII domain to obtain diverse outputs. Furthermore, a new constraint is present to encourage GANs to learn efficient mappings between AVI and AII domains by integrating geometry-consistency constraint and a dual learning framework, named dual geometry-consistency constraint. Coping with these two designs, our method exhibits superiority in both realism and diversity of the translation results over several state-of-the-art I2I translation methods on AVIID dataset and two new benchmark datasets for AVIT, which are obtained by extracting data from publicly available datasets. Code of DR-AVIT and proposed benchmark datasets are available at https://github.com/silver-hzh/DR-AVIT.