In the burgeoning field of virtual reality (VR) metaverse, the sophistication of interactions between robotic agents and their environment has become a critical concern. In this work, we present LWD-IUM, a novel light-weight detector designed to enhance robotic grasp capabilities in the VR metaverse. LWD-IUM applies deep learning techniques to discern and navigate the complex VR metaverse environment, aiding robotic agents in the identification and grasping of objects with high precision and efficiency. The algorithm is constructed with an advanced lightweight neural network structure based on self-attention mechanism that ensures optimal balance between computational cost and performance, making it highly suitable for real-time applications in VR. Evaluation on the KITTI 3D dataset demonstrated real-time detection capabilities (24-30 fps) of LWD-IUM, with its mean average precision (mAP) remaining 80% above standard 3D detectors, even with a 50% parameter reduction. In addition, we show that LWD-IUM outperforms existing models for object detection and grasping tasks through the real environment testing on a Baxter dual-arm collaborative robot. By pioneering advancements in robotic grasp in the VR metaverse, LWD-IUM promotes more immersive and realistic interactions, pushing the boundaries of what’s possible in virtual experiences.

Turbid underwater images often suffer from color distortion, contrast degradation, and detail loss. To improve the visual quality of these images, this paper proposes an illumination-constrained, structure-preserved retinex variational model. The proposed approach consists of three main components: a nonlinear model based on the classical retinex theory to represent the multiple adverse deformations of turbid underwater images; an adaptive channel compensation method to correct the color cast; and an illumination-constrained structure-preserved variational retinex model that simultaneously estimates a smooth illumination component and a detail display reflection component and uniformly predicts the noise pattern of preprocessed underwater images. Specifically, an adaptive weight matrix is proposed to reveal the structural details in reflectance. The overall smoothness of illumination is constrain by exponential guided filtering and l1/2 norm. The total intensity of the noise pattern is constrained by l2 norm. To solve the resulting optimization problem, we employ alternating direction minimization of logless transformations of Lagrange multipliers. Extensive experiments demonstrate the effectiveness of the proposed method in improving the quality of turbid underwater images. Beyond subjective visual observations, the method also exhibits competitive performance in objective image quality evaluations.

Underwater operations frequently encounter turbid environments, where light absorption and scattering by suspended particles degrade image quality by causing color distortion, uneven brightness, and blurred details. Clear imaging in such conditions is essential for enhancing the efficiency and effectiveness of underwater tasks, including exploration, marine ecological monitoring, and the preservation of underwater cultural heritage. However, existing underwater image enhancement methods struggle to perform well in turbid waters, especially in highly turbid conditions. In this study, we present an advanced method designed to significantly improve the clarity of images captured in turbid water. We begin by introducing an adaptive color correction algorithm that uses the dominant color channel’s pixel values to adjust and restore the colors of other channels, mitigating color distortion in turbid conditions. Subsequently, we apply adaptive threshold segmentation and turbidity assessment to automatically calibrate histogram equalization, which enhances local contrast and suppresses noise. Finally, we develop a dark channel prior based on turbidity background light estimation, which further improves color restoration and detail recovery. Our proposed method outperforms existing state-of-the-art techniques in color restoration, turbidity removal, and detail enhancement. Experimental results demonstrate that our approach effectively enhances imaging performance in turbid waters, thereby significantly improving the operational efficiency of various underwater applications.

In light of burgeoning technological progress and burgeoning labor deficits, the adoption of industrial robots has markedly intensified. These sophisticated automatons are pivotal in addressing the growing trend of high-mix, low-volume production, catering to the heterogeneous requisites of end-users. In this domain, it is imperative for industrial robots to facilitate automated bin picking, ensuring versatility and continuity in production workflows. Despite this, extant bin picking modalities fall short in discerning and orienting designated parts accurately. Our study introduces an avant-garde 3D object recognition framework, underpinned by deep learning algorithms, to streamline the bin picking process, obviating the necessity for human intervention. Moreover, while annotated data remains the cornerstone of deep learning paradigms, its procurement through conventional annotation is fraught with challenges. Addressing this bottleneck, we put forth a strategy that exploits training data autonomously generated within a simulation milieu, laying the groundwork for an object recognition model that eschews manual calibration. This model adeptly harnesses both bi-dimensional imagery and tri-dimensional point clouds to refine its recognition capabilities. Our empirical investigation, straddling simulated and authentic settings, substantiates the precision of our proposed methodology.

Imagery captured in extreme underwater environments often presents unique challenges, including blurred details, color distortion, and reduced contrast. These discrepancies largely emanate from the intricate interplay of light absorption and scattering within the aquatic medium. Predominant restoration techniques, rather simplistically, apply a static attenuation coefficient, neglecting the dynamic nuances of underwater conditions, leading to an inconsistent restoration outcome. To counter these impediments, we introduce an avant-garde Underwater Internet of Things (Underwater IoT) system, underpinned by a scene-depth fusion paradigm. Our methodology astutely accounts for the spectral decay of light underwater to infer a more refined attenuation coefficient tailored to the specific scene. This system, employing a quadtree decomposition for precise localization coupled with depth mapping, facilitates an astute estimation of prevailing luminescence. This depth map, once synthesized and refined, aids in gauging the precise attenuation dynamics of the aqueous milieu, culminating in a more precise transmission map derivation. Segueing from this, we employ an inverse model to refurbish the original image. Experimental results highlight our system’s prowess in counteracting issues like muddied details and chromatic anomalies while concurrently amplifying contrast. In juxtaposition with a spectrum of existing methodologies, our innovation outshines in terms of finesse and accuracy, underscoring its unparalleled efficacy in the challenging underwater conditions.

6D pose estimation of arbitrary objects is a crucial topic for intelligent transportation infrastructure measurement. However, some external environmental factors and the characteristics of the object itself impact the accuracy of the object’s pose estimation in practical applications. In this paper, we propose a new multi-class dataset ICD-4 (Industrial car Components Dataset) for 6D object pose estimation, which mainly includes four component categories, and every category takes 20,000 different scenarios. ICD-4 dataset delivers quite a few research challenges involving the range of object pose transformations and has significant research value for small-scale pose estimation tasks. We also propose an innovative method PoseMLP, a pose estimation network that uses residual MLP (multilayer perceptron) modules to predict the 6D pose estimation directly. Simultaneously, the experimental results demonstrate the effectiveness and reliability of the proposed method.

The problems of traditional traffic flow detection and calculation methods include limited traffic scenes, high system costs, and lower efficiency over detecting and calculating. Therefore, in this paper, we presented the updating Connected and Automated Vehicles (CAVs) model as the scanner of heterogeneous traffic flow, which uses various sensors to detect the characteristics of traffic flow in several traffic scenes on the roads. The model contains the hardware platform, software algorithm of CAV, and the analysis of traffic flow detection and simulation by Flow Project, where the driving of vehicles is mainly controlled by Reinforcement Learning (RL). Finally, the effectiveness of the proposed model and the corresponding swarm intelligence strategy is evaluated through simulation experiments. The results showed that the traffic flow scanning, tracking, and data recording performed continuously by CAVs are effective. The increase in the penetration rate of CAVs in the overall traffic flow has a significant effect on vehicle detection and identification. In addition, the vehicle occlusion rate is independent of the CAV lane position in all cases. The complete street scanner is a new technology that realizes the perception of the human settlement environment with the help of the Internet of Vehicles based on 5G communications and sensors. Although there are some shortcomings in the experiment, it still provides an experimental reference for the development of smart vehicles.

Recently, 3D object detection based on deep learning has achieved impressive performance in complex indoor and outdoor scenes. Among the methods, the two-stage detection method performs the best; however, this method still needs improved accuracy and efficiency, especially for small size objects or autonomous driving scenes. In this paper, we propose an improved 3D object detection method based on a two-stage detector called the Improved Point-Voxel Region Convolutional Neural Network (IPV-RCNN). Our proposed method contains online training for data augmentation, upsampling convolution and k-means clustering for the bounding box to achieve 3D detection tasks from raw point clouds. The evaluation results on the KITTI 3D dataset show that the IPV-RCNN achieved a 96% mAP, which is 3% more accurate than the state-of-the-art detectors.

Ships are usually disturbed by waves when they are traveling at sea. When the waves are large, it is not conducive to driving safety, comfort and economy. Therefore, this paper proposed a new type of automatic driving scheme, which links the wave height prediction with ship driving. By studying the accurate prediction of wave height, ships can adjust their course in real time to ensure that they always travel in the area with the lowest wave height. According to the different driving conditions of ships in the open sea and the offshore sea, we designed two wave height prediction models based on LSTM, which are suitable for the above two types of sea areas. In particular, when we created the open sea model, we selected the data of the location points other than the predicted point as the training data. After comparative testing, the two types of models have reached satisfactory accuracy, which provided support for the ship automatic driving scheme proposed in this paper.

Registration performs an individual and deciding role in multiple intelligent transport systems. The advancement of deep-learning-based methods enhances the robustness and effectiveness of the preliminary registration stage, although the algorithm will effortlessly fall into local optima when improving the ultimate exactitude. Similarly, traditional method based on optimization has a more reliable performance in terms of precision. However, its performance still counts on the quality of initialization. In order to solve the above problems, we propose a PBNet that combines a point cloud network with a global optimization method. This framework uses the feature information of objects to perform high-precision rough registration and then searches the entire 3D motion space to implement branch-and-bound and iterative nearest point methods. The evaluation results show that PBNet significantly reduce the influence of initial values on registration and has good robustness against noise and outliers.