PSiamRML: Target Recognition and Matching Integrated Localization Algorithm Based on Pseudo-Siamese Network

Multiobject detection tasks in complex scenes have become an important research topic, which is the basis of other computer vision tasks. Considering the defects of the traditional single shot multibox detector (SSD) algorithm, such as poor small object detection effect, reliance on manual setting for default box generation, and insufficient semantic information of the low detection layer, the detection effect in complex scenes was not ideal. Aiming at the shortcomings of the SSD algorithm, an improved algorithm based on the adaptive default box mechanism (ADB) is proposed. The algorithm introduces the adaptive default box mechanism, which can improve the imbalance of positive and negative samples and avoid manually set default box super parameters. Experimental results show that, compared with the traditional SSD algorithm, the improved algorithm has a better detection effect and higher accuracy in complex scenes.

We propose a lightweight detection algorithm based on the Single Shot MultiBox Detector (SSD) algorithm in order to facilitate sheep management and to realize sheep facial identification, and we take the self-constructed dataset as the research object. First, the SSD replaces the VGG16 backbone network with MobileNetv3, a lightweight neural network, to create a hybrid model that is much smaller. Second, the ECA attention mechanism is incorporated into the backend of the 72 × 160 bottleneck layer. Finally, the SmoothL1 loss function is substituted with the BalancedL1 loss function. The optimized model’s size decreases significantly from the original SSD’s 132 MB to just 22.4 MB. It achieves a mean average precision of 83.47% and maintains an average frame rate of 68.53 frames per second. Compared to the basic SSD model, the mean average precision has increased by 3.25 percentage points, the model size has decreased by 109.6 MB, and the detection speed has improved by 9.55 frames per second. In comparative experiments using the same dataset with different object detection models, the proposed model outperforms the SSD, Faster R-CNN, Retinanet, and CenterNet in terms of mean average precision, with improvements of 3.25 percentage points, 4.71 percentage points, 2.38 percentage points, and 8.13 percentage points, respectively. The detection speed has shown significant improvements, increasing by 9.55, 58.55, 53.1, and 12.37 frames per second, respectively. The improved model presented in this paper significantly reduces the model’s size and computational requirements while maintaining an excellent performance. This provides a valuable reference for the digitalization of animal husbandry and livestock farming.

SSD (Single Shot Multi Box Detector) is an object detection algorithm based on deep learning. As one of the most mainstream detection algorithms, it can greatly improve the detection speed and ensure the detection accuracy. In this paper, the Batch Norm operation is added to the network in order to improve the generalization of the network and speed up network training. The object counting function is added to the image recognition. This paper uses SSD algorithm that incorporates Batch Norm algorithm. The object detection system was built by the Flask framework and the Layui framework. The system can select the data to be detected on the front-end page, the detection results and the number of each type of object were displayed on the front-end page in real time.

The accuracy of English pronunciation is the key index to evaluate the quality of English teaching. Correct pronunciation and smooth language flow are the expectations of every student for English learning. Aiming at the poor effect and slow speed of the original SSD (Single Shot MultiBox Detector) algorithm in English teaching pronunciation detection, this paper proposes a clustering and improved SSD algorithm for English teaching pronunciation detection and recognition. The algorithm improves the concept module to enhance the feature extraction ability of the network and improve the detection speed. Meanwhile, it integrates multiscale features to realize multilayer multiplexing and equalization of features, so as to improve the detection effect of small target sound. This algorithm extracts more features by introducing channel attention mechanism, which increases the detection accuracy while reducing computation. In order to optimize the network’s ability to extract target location information, K-means clustering method is used to set the default parameters that are more in line with the characteristics of target samples. The experimental results showed that the proposed algorithm can accurately evaluate the pronunciation quality of reading aloud, so as to effectively reflect the oral English level of the reader.

Potholes are cracks on the road’s surface that leave a hole behind it. Reporting potholes to accountable bodies at an early stage can save many lives. Therefore, timely inspection and maintenance of potholes are required for smooth transportation. Traditional pothole detection methods are labor-intensive and time-consuming. This research focuses on such gaps and presents an intelligent detecting system that uses a smartphone camera, sensors, and gyroscope for real-time detection of potholes. The proposed model covers two essential functions: i). automated identification of potholes, and ii). notifying users to escape probable accidents. The “Single Shot Multi-Box Detector (SSD)” technique trains the pothole image datasets. For developing the dataset, pothole images are taken and labeled with TensorFlow object detection API. The method achieved 90% accuracy in detecting potholes in used image datasets. Study outcomes can help stakeholders get road profiling information and alerts about potholes for smooth road transportation.

An intelligent water level monitoring method based on SSD algorithm

In this paper, a novel three dimensional (3D) positioning framework of fluid antenna system (FAS)-enabled unmanned aerial vehicles (UAVs) is developed. In the proposed framework, a set of controlled UAVs including an active UAV and four FAS-enabled passive UAVs cooperatively estimate the real-time 3D position of a target UAV. Here, the active UAV transmits a measurement signal to the passive UAVs via the reflection from the target UAV. Each passive UAV estimates the distance of the active-target-passive UAV link and selects an antenna port to share the distance information with the base station (BS), which calculates the real-time position of the target UAV. As the target UAV is moving due to its task operation, the controlled UAVs must optimize their trajectories and select optimal antenna port for transmitting the positioning information, aiming to estimate the real-time position of the target UAV. We formulate this problem as an optimization problem whose goal is to minimize the target UAV positioning error via optimizing the trajectories of all controlled UAVs and antenna port selection of passive UAVs. To address this problem, an attention-based recurrent multi-agent reinforcement learning (AR-MARL) scheme is proposed, which enables each controlled UAV to use the local Q function to determine its trajectory and antenna port while optimizing the target UAV positioning performance without knowing the trajectories and antenna port selections of other controlled UAVs. Different from current MARL methods that use feedforward neural networks to approximate Q functions, the proposed method uses a recurrent neural network (RNN) that incorporates historical state-action pairs of each controlled UAV, and an attention mechanism to analyze the importance of these historical state-action pairs, thus improving the global Q function approximation accuracy and the target UAV positioning accuracy. Simulation results show that the proposed scheme can reduce the average positioning error by up to 17.5% and 58.5% compared to the value decomposition based-MARL scheme with FAS and the proposed AR-MARL method without FAS.

This paper describes an accurate attitude determination technique using multiple global navigation satellite system (GNSS) receivers and antennas for small unmanned aerial vehicles (UAVs). A laser survey using a small UAV needs highly accurate position and attitude information for reconstructing 3D models. The proposed technique uses the multiple GNSS antenna's positions computed by the GNSS carrier phase measurements to estimate the UAV's position and attitude. To solve the GNSS carrier phase ambiguity, the reliable ambiguity search is performed with a couple of constraints for the base line vector. The performance of the proposed technique is evaluated in the static test. From the test, the proposed technique could estimate UAV's attitude in 0.1 degree accuracy. Furthermore, the accurate UAV's position could be estimated in almost 100 % availability.

Small object detection is a challenging computer vision problem due to their low feature representation in the images and factors such as occlusions and noise. In images captured from a camera mounted on an unmanned aerial vehicle (UAV), objects are usually acquired in small sizes depending on the UAV flight altitude. The state-of-the-art object detectors often have lower detection accuracy with small objects. New approaches of combining features at multi-levels in the network helps in improving the object detection performance. In this paper, we propose a multi-scale approach of low-level feature combinations with deconvolutional modules on a single shot multibox detection (SSD) object detector to improve the small object detection in images acquired from a UAV. The proposed SSD based architecture is evaluated on UAV datasets to compare its performance with the state-of-the-art detectors.

With technological development of multi sensors, UAV (unmanned aerial vehicle) can identify and locate key targets in essential monitoring areas or geological disaster-prone areas by taking video sequence images, and precise positioning of the video sequence images is constantly a matter of great concern. In recent years, precise positioning of aerial images has been widely studied. But it is still a challenge to simultaneously realize precise, robust and dynamic positioning of UAV’s patrolling video sequence images in real time. In order to solve this problem, a visual positioning model for patrolling video sequence images based on DOM rectification is proposed, including a robust block-matching algorithm and a precise polynomial-rectifying algorithm. First, the robust block-matching algorithm is used to obtain the best matching area for UAV’s video sequence image on DOM (Digital Orthophoto Map), a pre-acquired digital orthophoto map covering the whole UAV’s patrolling region. Second, the precise polynomial-rectifying algorithm is used to calculate accurate rectification parameters of mapping UAV’s video sequence image to the best matching area obtained above, and then real time positioning of UAV’s patrolling video sequence images can be realized. Finally, the above two algorithms are analyzed and verified by three practical experiments, and results indicate that even if spatial resolution, surface specific features, illumination condition and topographic relief are significantly different between DOM and UAV’s patrolling video sequence images, proposed algorithms can still steadily realize positioning of each UAV’s patrolling video sequence image with about 2.5 m level accuracy in 1 s. To some extent, this study has improved precise positioning effects of UAV’s patrolling video sequence images in real time, and the proposed mathematical model can be directly incorporated into UAV’s patrolling system without any hardware overhead.

Manipulating objects using arms mounted to unmanned aerial vehicles (UAVs) is attractive because UAVs may access many locations that are otherwise inaccessible to traditional mobile manipulation platforms such as ground vehicles. Historically, UAVs have been employed in ways that avoid interaction with the environment at all costs. The recent trend of increasing small UAV lift capacity and the reduction of the weight of manipulator components make the realization of mobile manipulating UAVs imminent. Despite recent work, several major challenges remain to be overcome before it will be common practice to manipulate objects from UAVs. Among these challenges, the constantly moving UAV platform and compliance of manipulator arms make it difficult to position the UAV and end-effector relative to an object of interest precisely enough for reliable manipulation. Solving this challenge will bring UAVs one step closer to being able to perform meaningful tasks such as infrastructure repair, disaster response, law enforcement, and personal assistance. Toward a solution to this challenge, this thesis describes a way forward that uses the UAV as a means to crudely position a manipulator within reach of the end-effector's goal position in the world. The manipulator then performs the fine positioning of the end-effector, rejecting position perturbations caused by UAV motions. An algorithm to coordinate the redundant degrees of freedom of an aerial manipulation system is described that allows the motions of the manipulator to serve as inputs to the UAV's position controller. To demonstrate this algorithm, the manipulator's six degrees of freedom are servoed using visual sensing to drive an eye-in-hand camera to a specified pose relative to a target while treating motions of the host platform as perturbations. Simultaneously, the host platform's degrees of freedom are regulated using kinematic information from the manipulator. This ultimately drives the UAV to a position that allows the manipulator to assume a pose relative to the UAV that maximizes reachability, thus facilitating the arm's ability to compensate for undesired UAV motions. Maintaining this loose kinematic coupling between the redundant degrees of freedom of the host UAV and manipulator allows this type of controller to be applied to a wide variety of platforms, including manned aircraft, rather than a single instance of a purpose-built system. As a result of this loose coupling, careful consideration must be given to the manipulator design so that it can achieve useful poses while minimally influencing the stability of the host UAV. Accordingly, the novel application of a parallel manipulator mechanism is described.

With the continuous development of technology, the application of computer vision technology in cost monitoring of construction projects is becoming increasingly important. By utilizing computer vision technology, construction companies can monitor the progress of construction sites, material usage, and allocation of human resources in real-time, thereby better controlling project costs. The application of this technology can not only improve the efficiency of construction projects, but also reduce human errors and waste, which is of great significance for the successful completion of projects. Therefore, construction companies should actively adopt computer vision technology to improve the efficiency and accuracy of cost monitoring. In recent years, China's economy has continued to grow, continuously driving the development of infrastructure and manufacturing industries. However, at the same time, the production safety situation in China is becoming increasingly severe, and safety accidents continue to occur. One of the main causes of safety accidents is human misconduct, which includes workers wearing uniforms and safety helmets improperly during construction. Workers engage in some dangerous behaviors during construction, such as making phone calls, falling, and squatting for long periods of time. In response to the above issues, this article studies how the SSD (Single Shot MultiBox Detector) algorithm, CNN (Convolutional Neural Networks) algorithm, and YOLO (You only look once) algorithm can be applied to the behavior detection of construction workers, and compares them with existing object detection algorithms through experiments. The experimental data shows that the AP (Average precision), AP50, and AP75 values of the SSD-CNN algorithm in this article are 51.29%, 69.85%, and 54.81%, respectively. Among all the algorithms, the values of the three indicators rank in the top few.

In recent years, unmanned aerial vehicle (UAV) has been widely used in target tracking tasks such as security, anti-terrorism, tracking and monitoring because of its wide and flexible perspective. At the same time, deep learning creates new conditions for UAV visual recognition and motion tracking because it has high recognition accuracy and fast recognition speed. This paper presents an UAV dynamic tracking algorithm based on deep learning. The algorithm mainly includes three parts: the PTZ control, relative position calculation and the UAV control. The main work includes: 1. The tracking target is identified through the deep learning network based on YOLOv3, and the PTZ angle is adjusted in real time by using the control method of combining the PID control with the time-delay control to reduce the impact of image transmission delay; 2. The relative position between the UAV and the target is calculated by combining the UAV flight altitude, UAV attitude, PTZ angle and the coordinate of the target in the image. At the same time, the relative position prediction is added to estimate the relative distance between the UAV and the target at the next frame time as a reference value of the UAV speed, so as to improve the dynamic tracking ability; 3. The relative position information is fed back to the flight control function, and the linear velocity of UAV is output by the PID controller. Through the simulation test in Airsim, it is proved that the proposed algorithm can effectively improve the stability and robustness of the UAV tracking system.

Unmanned aerial vehicles (UAVs) have been considered as a solution to extend coverage for disaster-resilient communication networks in a disaster area when the communication infrastructure which is known as the base station (BS) cannot afford the service as usual. However, when the link between the UAV and the serving BS is constrained, the UAV must keep the appropriate distance for reliable transmission. In addition, the altitude of the UAV may significantly affect the link quality between the UAV and the BS, as well as the link between the UAV and the users/rescuers. In this paper, we analyze the coverage of the disaster-resilient communication network with a UAV for guaranteeing the reliability of all links. We show that the position of UAV can be a function of the BS's and user's elevation angle. The simulation results show that the maximum radius of coverage in the high-rise urban environment can be 5.25 km and 2.46 km for downlink and uplink, respectively. Moreover, this paper can provide a principle for the central emergency operation center (CEOC) to immediately deploy the disaster-resilient communication network.

In recent years, with the rapid development of satellite technology, object detection technology in remote sensing images has become an important research direction in the field of image processing. In this paper, aiming at the problems of low resolution, slow detection speed and vulnerability to background interference in remote sensing satellite images of ground vehicles, a vehicle small object detection algorithm based on residual network is proposed. Based on the original SSD (Single Shot MultiBox Detector) algorithm, the backbone network adopts the residual network with stronger feature extraction ability and less parameters, which can effectively improve the detection accuracy of small targets; Meanwhile, the Dense Extra Feature Layers network is proposed to enhance Feature propagation. Experimental results show that the mAP value of the proposed method in vehicle remote sensing image detection reaches 0.573, and the detection speed is 30.1fps, which has a good improvement in the detection accuracy and speed.