Aerial object detection acts a pivotal role in searching and tracking applications. However, the large model, limited memory, and computing power of embedded devices restrict aerial pedestrian detection algorithms’ deployment on the UAV (unmanned aerial vehicle) platform. In this paper, an innovative method of aerial infrared YOLO (AIR-YOLOv3) is proposed, which combines network pruning and the YOLOv3 method. Firstly, to achieve a more appropriate number and size of the prior boxes, the prior boxes are reclustered. Then, to accelerate the inference speed on the premise of ensuring the detection accuracy, we introduced Smooth-L1 regularization on channel scale factors, and we pruned the channels and layers with less feature information to obtain a pruned YOLOv3 model. Meanwhile, we proposed the self-built aerial infrared dataset and designed ablation experiments to perform model evaluation well. Experimental results show that the AP (average precision) of AIR-YOLOv3 is 91.5% and the model size is 10.7 MB (megabyte). Compared to the original YOLOv3, its model volume compressed by 228.7 MB, nearly 95.5 %, while the model AP decreased by only 1.7%. The calculation amount is reduced by about 2/3, and the inference speed on the airborne TX2 has been increased from 3.7 FPS (frames per second) to 8 FPS.

Visual servoing is a control method that utilizes image feedback to control robot motion, and it has been widely applied in unmanned aerial vehicle (UAV) motion control. However, due to field-of-view (FOV) constraints, visual servoing still faces challenges, such as easy target loss and low control efficiency. To address these issues, visual servoing control for UAVs based on the deep reinforcement learning (DRL) method is proposed, which dynamically adjusts the servo gain in real time to avoid target loss and improve control efficiency. Firstly, a Markov model of visual servoing control for a UAV under field-of-view constraints is established, which consists ofquintuplet and considers the improvement of the control efficiency. Secondly, an improved deep Q-network (DQN) algorithm with a target network and experience replay is designed to solve the Markov model. In addition, two independent agents are designed to adjust the linear and angular velocity servo gains in order to enhance the control performance, respectively. In the simulation environment, the effectiveness of the proposed method was verified using a monocular camera.

Visual-based object detection and understanding is an important problem in computer vision and signal processing. Due to their advantages of high mobility and easy deployment, unmanned aerial vehicles (UAV) have become a flexible monitoring platform in recent years. However, visible-light-based methods are often greatly influenced by the environment. As a result, a single type of feature derived from aerial monitoring videos is often insufficient to characterize variations among different abnormal crowd behaviors. To address this, we propose combining two types of features to better represent behavior, namely, multitask cascading CNN (MC-CNN) and multiscale infrared optical flow (MIR-OF), capturing both crowd density and average speed and the appearances of the crowd behaviors, respectively. First, an infrared (IR) camera and Nvidia Jetson TX1 were chosen as an infrared vision system. Since there are no published infrared-based aerial abnormal-behavior datasets, we provide a new infrared aerial dataset named the IR-flying dataset, which includes sample pictures and videos in different scenes of public areas. Second, MC-CNN was used to estimate the crowd density. Third, MIR-OF was designed to characterize the average speed of crowd. Finally, considering two typical abnormal crowd behaviors of crowd aggregating and crowd escaping, the experimental results show that the monitoring UAV system can detect abnormal crowd behaviors in public areas effectively.

We explore the feasibility of a controllable and easy-to-implement moiré-based composite circular-line gratings imaging scheme for optical alignment in proximity lithography. One circular grating and four line gratings located on both the mask alignment mark and wafer alignment mark are used to realize the coarse alignment and fine alignment respectively. The fundamental derivation of coarse alignment employing circular gratings and fine alignment employing line gratings are given. Any displacement of misalignment that occurs at the surface of two overlapped gratings can be sensed and determined through subsequent fringe phase analysis without the influence of the gap between the mask and the wafer or wafer process. The design and manufacture process of the alignment marks are presented. The experimental results validate and demonstrate the feasibility of the proposed approach.

The use of quadrotors is proliferating across many civil applications, including those in the areas of the Internet of Things (IoT) and communications. Attitude control plays an important role in these applications, and designing a quadrotor attitude controller is a challenging task. Recently, several design methods for the attitude controller were presented with an accurate model. However, during the flying state, the quadrotor may be affected by several factors that cause unpredictable disturbances to the model. As a result, an accurate model of disturbances is difficult to establish. To address this problem, in this paper, a novel linear feedforward all-coefficient adaptive control (FACAC) strategy based on characteristic modeling is proposed. The contributions of our work are as follows. First, a second-order characteristic model of the quadrotor attitude control is established based on the dynamic equations. Second, an all-coefficient adaptive control (ACAC) controller that consists of the golden-section adaptive control law, the logic integral, and logic differential control laws is designed. The feedforward maintaining and tracking control law is integrated into the ACAC controller to shorten the settling time. Finally, extensive simulation results demonstrate that the performance of the proposed FACAC controller outperforms the state-of-the arts.

One of the major agricultural disasters, which depressed crop production and lower-quality, is pest in China. The lack of technical and scientific knowledge to prevent pest diseases is the main reason for low productivity of these crop commodities. Traditional methods based on artificial judgment have the disadvantages of large workload, low efficiency, poor working environment, and caused damage to crops. In response to the above problems, a HOG + SVM-based crop pest monitoring drone (PM-VAV) system was proposed, which utilizes computer vision combined with the flexibility of the drone for non-contact measurement. Firstly, the real aircraft working platform of this PM-UAV system was constructed. Then, through self-built data sets satisfy the requirements of the model training for pest detection task. Secondly, the on-line monitoring task of crop pests was accomplished through the use of the Histogram of Oriented Gradient (HOG) feature and Support Vector Machine (SVM) classifier, the algorithm was ported to the airborne embedded platform NVIDIA TK1. Finally, experimental tests show that the designed monitoring aircraft can effectively implement on-line monitoring for the crop pest which contained in the self-built data set.