Infrared (IR) target detection is crucial for IR search and tracking (IRST) systems in maritime environments. Generally, the size of the target varies as the distance changes, making it challenging to detect in a single traversal. Additionally, small and dim targets may possess morphological features that have not been fully exploited yet. To address this, we propose an efficient maritime IR target detection method based on grid saliency and morphological filtering (MF). First, to initially determine the target area, we segment the sea surface using sea-sky line (SSL) detection. Second, we devise a further sub-region segmentation strategy to extract candidate targets within respective grids, thereby avoiding the influence of unrelated areas. Third, to fully leverage the shape information of maritime targets, based on corresponding morphological analysis, we introduce a pair of specially designed morphological filters for final target enhancement and interference suppression. Finally, targets can be readily detected by searching for connected domains in the binary map. Experiments performed on two maritime target detection datasets have demonstrated that our proposed method can achieve an average precision (AP) exceeding 94% on the majority of the image sequences. Furthermore, it boasts a detection speed surpassing 36 frames per second (FPS), even when operated on a single CPU, surpassing the current state-of-the-art methods. Furthermore, we also investigated the relationship between dataset size and the performance of learning-based methods and found that non-learning methods can achieve superior detection results when sample sizes are limited.

Existing human pose estimation methods seldom consider the impact or constraint of different types of contact. In this paper, we elaborate on the impact of both body-scene contact and self-contact on pose estimation and refer to them as general contact. First, we extend existing datasets by calculating additional contact labels for general contact inference. Moreover, based on the extended dataset, we present the first network to predict dense general contact from a single RGB image. Finally, we develop a novel optimization method that successfully utilizes the inferred general contact information for accurate 3D pose estimation. Our results show that knowledge of contact can provide strong constraints and resolve pose ambiguity, thus significantly improving human pose estimation accuracy, especially for challenging poses that cannot be well handled by existing methods. Experimental results and comparisons further demonstrate the effectiveness of the proposed method. Our results are even more reasonable than certain pseudo-ground truth determined from multi-view images.

Infrared (IR) ship detection in autonomous aerial vehicle (AAV) imagery under maritime environments encounters challenges due to the complexity of sea-sky backgrounds. Current research efforts have not fully leveraged the unique characteristics of maritime scenes to distinguish this task from general IR target detection. To overcome these limitations, we propose a two-stage sea-sky region-segmentation-guided IR ship enhancement and detection (TIRSD) learning network, specifically designed for targeted ship enhancement and detection in maritime environments. The process is structured into two main stages: enhancement and detection. In the enhancement stage, pixel-level sea-sky region segmentation is accomplished via sea-sky line (SSL) detection, which is followed by precise IR ship enhancement and interference suppression, utilizing specially designed morphological filters. For the detection stage, the bilateral hierarchical target encoding module (BHTEM) is introduced to encode both high- and low-dimensional features, ensuring optimal detail retention and extraction. Additionally, the iterative fusion enhancement midlayer (IFEM) with neighborhood reinforcement operations is presented. Furthermore, a newly controllable local discrete attention mechanism (LDAM) is utilized to focus on small ships, thereby improving detection efficiency. TIRSD surpasses the current leading methods in terms of accuracy, achieving a mean average precision (mAP) of 76.9% on the proposed dataset and 52.4% on a public dataset. Comprehensive experimental results underscore TIRSD's effective utilization of the unique characteristics of maritime environments, indicating promising application prospects.

Existing self-contact detection methods have difficulty detecting dense per-vertex self-contact. Dataset collection for existing self-contact detection methods is costly and inefficient, as it requires different subjects to mimic the same pose. In this paper, we propose a generation-to-generalization approach by utilizing ControlNet to augment existing datasets. Based on that we develop a keypoint-conditioned neural network that can successfully infer per-vertex self-contact from a single image. With the extended dataset synthesized by ControlNet, our network requires only one real subject training data to achieve satisfactory individual generalization ability. Experiments verify the effectiveness of our proposed method and the improvement of the network’s generalization with synthetic data.

Foot contact is an important cue for human motion capture, understanding, and generation. Existing datasets tend to annotate dense foot contact using visual matching with thresholding or incorporating pressure signals. However, these approaches either suffer from low accuracy or are only designed for small-range and slow motion. There is still a lack of a vision-pressure multimodal dataset with large-range and fast human motion, as well as accurate and dense foot-contact annotation. To fill this gap, we propose a Multimodal MoCap Dataset with Vision and Pressure sensors, named MMVP. MMVP provides accurate and dense plantar pressure signals synchronized with RGBD observations, which is especially useful for both plausible shape estimation, robust pose fitting without foot drifting, and accurate global translation tracking. To validate the dataset, we propose an RGBD-P SMPL fitting method and also a monocular-video-based baseline framework, VP-MoCap, for human motion capture. Experiments demonstrate that our RGBD-P SMPL Fitting results significantly outperform pure visual motion capture. Moreover, VP-MoCap outperforms SOTA methods in foot-contact and global translation estimation accuracy. We believe the configuration of the dataset and the baseline frameworks will stimulate the research in this direction and also provide a good reference for MoCap applications in various domains. Project page: https://metaverse-ai-lab-thu.github.io/MMVP-Dataset/

Fast and accurate infrared (IR) sea–sky line region (SSLR) detection can improve the early warning capability of the small targets that appear in the remote sea–sky junction. However, the traditional algorithms struggle to achieve high precision, while the learning-based ones have low detection speed. To overcome these problems, a novel learning-based algorithm is proposed; rather than detecting the sea–sky line first, the proposed algorithm directly provides SSLR, which mainly consists of three parts: Firstly, an IR sea–sky line region detection module (ISRDM) is proposed, which combines strip pooling and the connection mode of a cross-stage partial network to extract the features of the SSLR target, with an unbalanced aspect ratio, more specifically, thus improving the detection accuracy. Secondly, a lightweight backbone is presented to reduce the parameters of the model and, therefore, improve the inference speed. Finally, a Detection Head Based on the spatial-aware attention module (SAMHead) is designed to enhance the perception ability of the SSLR and further reduce the inference time. Extensive experiments conducted on three datasets with more than 26,000 frames show that the proposed algorithm achieved approximately 80% average precision (AP), outperforms the state-of-the-art algorithms in accuracy, and can realize real-time detection.

The existing infrared (IR) sea-sky line (SSL) detection algorithms usually treat the SSL as a straight line and, therefore, cannot handle the curved SSL caused by lens distortion and earth curvature. To address this issue, a simple but efficient one-stage end-to-end network termed CSSLNet that is specially designed for curved IR SSL detection is proposed, which treats the SSL as a curve and achieves accurate detection by directly predicting the coefficients of the polynomial. First, by predicting the fitting coefficients, a quadratic polynomial is obtained to describe the curved SSL. Then, to solve the sway problem of the curve, the intersection points of the SSL and image boundaries are used as additional weights to provide an overall constraint of the curve. Finally, to solve the problem of insufficient training samples of the curved SSL, an image augmentation method is employed to enlarge the dataset by performing warping operations on real IR SSL images. The experiments based on the dataset which includes natural straight, natural curve, and artificially curved SSL indicated that the proposed algorithm could achieve precise detection on both straight and curved SSL, which is comparable with the state-of-the-art in precision and speed. Moreover, the CSSLNet is the first algorithm that is specially designed for curved SSL detection, which can be a baseline for future research.

Fast and accurate infrared (IR) sea-sky line (SSL) detection could greatly benefit the efficiency of target detection under maritime. However, the traditional SSL detection algorithms are slightly inferior in detection accuracy, while the convolutional neural network (CNN)-based algorithms have high standard hardware and dataset requirements, which are difficult to satisfy in some practical scenes. In this article, a novel concise and intuitive SSL detection algorithm named bisection window and homogeneity principle-based local contrast measure (BHLCM) is proposed. First, a bisection local contrast window (BLCW) is proposed based on the local contrast measure (LCM) and be used for the search of the patches that contain SSL segments along a set of variable preset vertical paths. Then, based on the analysis of the physical characteristics of areas near SSL, the homogeneity principle is proposed to remove the patches containing false SSL segments. Finally, the midpoints of the remaining SSL segments are used to fit the final SSL through random sample consensus (RANSAC). Experimental results based on four IR image sequences with more than 3000 IR images illustrate that compared with the state-of-the-art algorithms, BHLCM not only achieves precision comparable to the most accurate one but also is significantly ahead in speed among most of the algorithms compared. In addition, speed tests about BHLCM based on different hardware platforms show that real-time detection could still be achieved even without high-performance graphics cards. The code and dataset are available at BHLCM https://github.com/FJsRepo/BHLCM.

Infrared (IR) small dim target detection near the sea–sky line (SSL) is crucial for enhancing the early warning capability of maritime vehicles. However, the interferences caused by the strong contrast have not been properly addressed. Consequently, a specially designed algorithm regional-division local contrast measure (RDLCM) that focuses on the detection of IR small dim targets appearing near the SSL is proposed. First, an SSL detection module based on a lightweight convolutional neural network (CNN) is devised to achieve fast pixel-level SSL detection. Then, a set of regional-division windows (RDWs) are designed according to the strong grayscale contrast distribution around the SSL, and through the division of the effective regions, the RDWs could realize the potential extraction and refinement of the IR small dim targets that appear near the SSL. Experiments on three IR image sequences demonstrate that the proposed algorithm achieves the best detection accuracy among the classical and state-of-the-art algorithms in comparison and runs at 44 frames per second (FPS), which could meet real-time requirements. The code and dataset are available at RDLCM.

In this paper, we propose a controllable high-quality free viewpoint video generation method based on the motion graph and neural radiance fields (NeRF). Different from existing pose-driven NeRF or time/structure conditioned NeRF works, we propose to first construct a directed motion graph of the captured sequence. Such a sequence-motion-parameterization strategy not only enables flexible pose control for free viewpoint video rendering but also avoids redundant calculation of similar poses and thus improves the overall reconstruction efficiency. Moreover, to support body shape control without losing the realistic free viewpoint rendering performance, we improve the vanilla NeRF by combining explicit surface deformation and implicit neural scene representations. Specifically, we train a local surface-guided NeRF for each valid frame on the motion graph, and the volumetric rendering was only performed in the local space around the real surface, thus enabling plausible shape control ability. As far as we know, our method is the first method that supports both realistic free viewpoint video reconstruction and motion graph-based user-guided motion traversal. The results and comparisons further demonstrate the effectiveness of the proposed method.

The infrared (IR) horizon is an essential reference for near-ground state awareness and target detection. The traditional horizon detection algorithms based on hand-crafted features claim the advantage of being intuitive and highly targeted, however, their capability of detail extraction and robustness is not strong enough when switching among diverse scenes, meanwhile, the convolutional neural networks (CNNs) have the powerful ability in feature acquisition, but has the disadvantages of interpretability and instability. Given this, this article proposes a novel horizon detection algorithm that combines the traditional method and CNN. First, the hyper-Laplace filter (HLF) is proposed to eliminate the interferences and enhance the saliency of the target. Then a positioning module (PM) based on the traditional idea which fully utilizes the structural characteristics of multifeature-maps in CNN is present to extract the endpoints of the horizon. Finally, an auxiliary module that consists of a revise branch (RB) and self-attention module (SAM) is designed to assist the PM with detailed corrections. Compared with the state-of-the-art algorithms, experiments based on three datasets with more than 25000 frames under various scenes demonstrate that the proposed algorithm not only achieves the best accuracy and highest stability but also addresses real-time needs. Notably, the detection deviation is controlled within 2 pixels on all three datasets while achieving a comparable detection speed of 170 frames/s. Besides, our algorithm also shows strong robustness even under harsh sea conditions and urban backgrounds. Code and trained models are available at https://github.com/FJsRepo/InfML-HDD.

Aiming at the demand of assembly assistance for complex industrial products, this paper studies the structure and key links of augmented reality aided assembly system, and takes a gear pump as an example to realize the parts detection function based on yolo neural network algorithm. On this basis, the assembly assistance system is built based on Vuforia, and the user interface is designed, realizing the functions of assembly progress identification and assembly information prompt.

差分放大电路是模拟电子技术课程中的一个重要知识点.传统教学模式下,差分放大电路一般先求解静态工作点,然后根据信号的双端或单端输入方式以及输出方式,将输入信号等效为共模信号与差模信号,再结合电路的对称性,分别分析差分放大电路的差模特性与共模特性[1].目前,国内外教材与文献中,对差分放大电路交流通路等效方法主要有两种,一种是数学等效[1-2],另一种是输入信号耦合传输等效[3-4],这两种等效方法均引入了差模与共模概念,由于放大电路信号的激励和输出方式组合共4种,学生习惯于单入单出的电路模型分析,理解上难度较大,公式容易混淆.本文提出一种新的差分放大电路的交流分析通用模型,利用该模型可推导出差分放大电路的一般性结果,与教材上对比分析,有助于学生深入理解差分放大的概念并掌握电路的内在本质.

Infrared and visible image fusion could be widely used in military, navigation and medical fields. Infrared image contains thermal radiation information while visible image contains detailed textures of the target and scene. Information elements from these images are complementary. The fusion aims to transfer as much complementary information as possible to fused image. Therefore, the fused image could achieve a better performance than source images in applications such as target detection and camouflage recognition. In this paper, a new method based on anisotropic diffusion and weight map construction is proposed. Considering the characteristic differences between infrared and visible images, different parameters of anisotropic diffusion are used when decomposing those images. Either image is decomposed into base layer and detail layer after anisotropic diffusion. Different rules of weight map construction is used to fuse base layer and detail layer, respectively. Final fused image is generated through linear combination of fused base layer and detail layer. Experiments are conducted to compare proposed method with the traditional and other recently proposed methods according to subjective and objective evaluation criterion. Result shows that the performance of the proposed method is comparable or superior to other methods.

In this paper, the application of augmented reality technology aiming at the difficulty of parts assembly and disassembly in aviation equipment maintenance is studied. Considering parts recognition as one of the critical steps, the operations of image processing, feature extraction and feature matching are programmed, and the effects of SIFT and SURF feature extraction algorithms are compared. On this basis, two augmented reality tools, Vuforia and EasyAR, are used to build an assistance system for equipment maintenance and assembly. The user interface is designed to realize the display of image matching results and assembly tips. Through the practice above, the feasibility of intelligent maintenance technology using augmented reality technology is verified.

Infrared (IR) sea-sky line (SSL) detection is an important assisting method for maritime monitoring. In complex sea-sky backgrounds, strong clouds and waves often have IR features similar to the real SSL, which challenges the true positive rate (TPR) of SSL detection. We propose a new SSL detection algorithm based on a novel self-adaptive Laplacian of Gaussian (SALoG) filter and visual-saliency-based probabilistic Hough transform (VSBPHT). The Laplacian filter of the SALoG has a self-adaptive new kernel, which is calculated by the overall gray information of the image. Its proportional-gray-reduction feature can weaken the interferences, especially in the sky region, and relatively enhance the SSL simultaneously, so that we can detect the SSL from the top to the bottom of the image which could avoid the disturbances in the complex sea region. Also, in order to improve the processing speed and accuracy, the algorithm uses probabilistic Hough transform (PHT) to extract the candidate line segment set and refine it by SSL visual saliency features, the final segments are then used to fit the SSL. Experimental results based on three IR image sequences with different backgrounds show that the proposed algorithm has the highest TPR and precision compared with state-of-the-art algorithms, and the average time consumption could meet the needs for real-time processing as well.

In driver assistance systems, thermal cameras are often used because they can provide compensation information for other sensors in the case of darkness or glare. For many existing thermal image vehicle detection algorithms, they can get a good detection accuracy in some occasions but the detection speed is relatively slow so they can't meet the real-time requirements. In order to address this problem, we proposed a real-time thermal image vehicle detection algorithm based on yolov3-tiny. We have made two major improvements to the original yolov3-tiny. The first is to recalculate the anchor box priors by running k-means clustering algorithm on the bounding boxes of training dataset to make the network easier to learn. The second is to deepen the network structure of the original yolov3-tiny, so that it can better extract the characteristics of the vehicle in thermal images, so as to improve the vehicle detection accuracy. Our experimental results show that the mean Average Precision (mAP) of our proposed method is 6% higher than the origin yolov3-tiny while maintaining the detection speed comparable to the original yolov3-tiny.

A driving structure using the shape memory alloy (SMA) wire to deflect an aircraft control surface was designed in this study. Instead of changing angle mounts or other conventional methods for changing the attitude angle of aircraft control surfaces, this driving structure drew on the existing idea of smart material SMA driven aileron deformation. The main contribution of this study is the optimization of the rib design inside the control surface: ribs with different numbers of hinged sections were evaluated and the location of the SMA wires were adjusted, to improve the range of achievable deflection angles and the ability of the rib to withstand aerodynamic force. Simulation results show that the use of a three-section or four-section rib effectively improves the aerodynamic effect of the control surface over the use of a two-section rib, especially under the action of larger aerodynamic forces; compared with a two-section rib, a four-section rib provides a deflection angle as much as 20% larger under aerodynamic pressure.

This paper reports an advanced folded piezoelectric vibration energy harvester (PVEH) that has excellent power generation performance at the former two-order resonant frequencies. Its volume is ∼1 cm3 and its mass is less than 5 g. Extending piezoelectric transducer sheets can effectively avoid stress concentration and greatly improve the power generation performance of the device. Besides, by adjusting the height–length ratio of the folded structure under 0.35, the device can generate power effectively in the former two vibration modes. Finite element simulation analysis by COMSOL Multiphasic was performed to fabricate and test a series of PVEHs. Their resonant frequencies and peak output powers were analyzed. The advanced folded PVEH can harvest energy effectively at the former two resonant frequencies: 58 Hz and 136 Hz. A multi-line classical energy harvesting circuit is designed to harvest the energy generated by all bimorph beams. Under 0.4 g excitation acceleration, the two peak output voltages of the energy harvesting circuit are 19.4 V and 6.32 V and the two peak output powers of the energy harvesting circuit are 336.04 µW and 77.07 µW. In the former two-order vibration modes, the power densities are 2.1 mW/cm3/g2 and 0.48 mW/cm3/g2, respectively.

This paper proposes a new method for fast human motion capture based on a single RGB-D sensor. By leveraging the human pose detection results for reinitializing the ICP-based sequential human motion tracking algorithm when tracking failure happens, our system achieves a highly robust and accurate human motion tracking performance even for fast motion. Moreover, the calculation and utilization of semantic tracking loss enable body-part-level motion tracking refinement, which is better than whole-body refinement. Finally, a simple yet effective post-processing method, semantic bidirectional motion blending, for single-view human motion capture is proposed to further improve the tracking accuracy, especially under severe occlusions and fast motion. The results and experiments demonstrate that the proposed method achieves highly accurate and robust human motion capture performance in a very efficient way. Applications include AR&VR, human motion analysis, movie, and gaming.