To bolster the effectiveness of underwater object detection, a new detection methodology was formulated, comprising a novel detection neural network called TC-YOLO, an adaptive histogram equalization image enhancement technique, and an optimal transport scheme for label assignments. Antineoplastic and I inhibitor Drawing upon the architecture of YOLOv5s, researchers developed the TC-YOLO network. The new network's backbone integrated transformer self-attention, while the neck was equipped with coordinate attention, all to improve feature extraction relating to underwater objects. Implementing optimal transport label assignment yields a substantial decrease in fuzzy boxes and better training data utilization. Evaluated on the RUIE2020 dataset and through ablation experiments, the proposed underwater object detection technique demonstrates improvement over the YOLOv5s and similar networks. Concurrently, the model's footprint and computational cost remain minimal, aligning with requirements for mobile underwater applications.
Recent years have seen the escalation of subsea gas leaks, a direct consequence of the proliferation of offshore gas exploration, endangering human lives, corporate assets, and the environment. Optical imaging-based monitoring of underwater gas leaks is now prevalent, but substantial labor expenditures and false alarms are still significant challenges, stemming from the operators' procedures and judgment calls. This study sought to establish a sophisticated computer vision-based monitoring strategy for automated, real-time detection of underwater gas leaks. A study was conducted to analyze the differences and similarities between the Faster Region Convolutional Neural Network (Faster R-CNN) and the You Only Look Once version 4 (YOLOv4). In assessing the effectiveness of automatic and real-time underwater gas leakage monitoring, the Faster R-CNN model, operating on 1280×720 images without noise, emerged as optimal. Antineoplastic and I inhibitor This leading model successfully classified and located the precise position of underwater gas plumes, distinguishing between small and large-scale leaks, all from real-world data.
The proliferation of computationally demanding and time-critical applications has frequently exposed the limited processing capabilities and energy reserves of user devices. Mobile edge computing (MEC) effectively tackles this particular occurrence. MEC facilitates a rise in task execution efficiency by directing particular tasks for completion at edge servers. Within the context of a D2D-enabled MEC network communication model, this paper explores the subtask offloading approach and the corresponding power allocation for users. Minimizing the weighted sum of average user completion delay and average energy consumption constitutes the objective function, presenting a mixed-integer nonlinear optimization problem. Antineoplastic and I inhibitor Our initial approach for optimizing the transmit power allocation strategy involves an enhanced particle swarm optimization algorithm (EPSO). We then leverage the Genetic Algorithm (GA) for optimizing the subtask offloading strategy. Our proposed optimization algorithm (EPSO-GA) aims to optimize concurrently the transmit power allocation scheme and the subtask offloading plan. The EPSO-GA algorithm demonstrates superior performance against competing algorithms, resulting in lower average completion delays, energy consumption, and overall cost. The EPSO-GA approach demonstrates the lowest average cost, despite potential adjustments to the weighting factors related to delay and energy consumption.
High-definition imagery covering entire construction sites, large in scale, is now frequently used for managerial oversight. Nonetheless, the transmission of high-resolution images proves a significant hurdle for construction sites plagued by poor network conditions and constrained computational resources. Accordingly, there is an immediate need for an effective compressed sensing and reconstruction technique for high-definition monitoring images. Current image compressed sensing techniques leveraging deep learning, while superior in recovering images from reduced measurements, present a challenge in achieving efficient and accurate high-definition reconstruction for the demanding dataset of large construction site images with restricted computational and memory resources. In the context of large-scale construction site monitoring, this paper investigated an efficient deep learning-based high-definition image compressed sensing framework, EHDCS-Net. The architecture comprises four modules: sampling, initial reconstruction, the deep recovery unit, and the recovery head. The framework's exquisite design arose from a rational organization of the convolutional, downsampling, and pixelshuffle layers, all in accordance with block-based compressed sensing procedures. The framework strategically utilized nonlinear transformations on downsized feature maps in image reconstruction to effectively limit memory footprint and computational expense. In addition, the ECA channel attention module was incorporated to amplify the non-linear reconstruction capacity on the reduced-resolution feature maps. Images of a real hydraulic engineering megaproject, encompassing large scenes, were used in the testing of the framework. Experiments using the EHDCS-Net framework proved that it outperformed other current deep learning-based image compressed sensing methods by consuming fewer resources, including memory and floating-point operations (FLOPs), while delivering both better reconstruction accuracy and quicker recovery times.
Pointer meters, when used by inspection robots in intricate settings, are often affected by reflective occurrences, potentially impacting reading accuracy. A deep learning-informed approach, integrating an enhanced k-means clustering algorithm, is proposed in this paper for adaptive detection of reflective pointer meter areas, complemented by a robot pose control strategy designed to remove them. The process primarily involves three stages: first, a YOLOv5s (You Only Look Once v5-small) deep learning network is employed for real-time detection of pointer meters. The detected reflective pointer meters are preprocessed via a perspective transformation, a critical step in the process. The perspective transformation procedure is applied to the output derived from the deep learning algorithm and detection results. Using the YUV (luminance-bandwidth-chrominance) color spatial data of the acquired pointer meter images, the brightness component histogram's fitting curve and its associated peak and valley information are derived. Employing the provided data, the k-means algorithm is subsequently modified to dynamically establish its optimal cluster quantity and initial cluster centers. Employing a refined k-means clustering algorithm, the detection of reflections within pointer meter images is carried out. Reflective areas can be eliminated through a determined pose control strategy for the robot, considering its movement direction and distance covered. The proposed detection methodology is finally tested on an inspection robot detection platform, allowing for experimental assessment of its performance. Results from experimentation highlight that the proposed method possesses both excellent detection accuracy, reaching 0.809, and an exceptionally short detection time of 0.6392 seconds, compared to other comparable techniques documented in the literature. Inspection robots can benefit from this paper's theoretical and technical framework, which aims to mitigate circumferential reflections. The inspection robots' movements are dynamically adjusted to precisely and rapidly remove any reflective areas found on pointer meters. The potential of the proposed detection method lies in its ability to enable real-time reflection detection and recognition of pointer meters on inspection robots within complex settings.
In aerial monitoring, marine exploration, and search and rescue, the coverage path planning (CPP) of multiple Dubins robots is a widely employed technique. Exact or heuristic algorithms are commonly used in multi-robot coverage path planning (MCPP) research to address coverage. Area division, carried out with meticulous precision by certain exact algorithms, often surpasses the coverage path approach. Heuristic methods, however, frequently face a challenge of balancing desired accuracy against the demands of algorithmic complexity. In known environments, this paper explores the Dubins MCPP problem. A mixed-integer linear programming (MILP)-based exact Dubins multi-robot coverage path planning algorithm, designated as EDM, is presented. The Dubins coverage path of shortest length is found by the EDM algorithm through a comprehensive search of the entire solution space. Furthermore, a heuristic approximation of credit-based Dubins multi-robot coverage path planning (CDM) is introduced, leveraging a credit model to distribute tasks among robots and a tree-partitioning strategy to simplify the process. Comparisons of EDM with other exact and approximate algorithms show that EDM minimizes coverage time in limited scenes, and CDM achieves a shorter coverage time with reduced computational effort in extensive scenes. Through feasibility experiments, the applicability of EDM and CDM to high-fidelity fixed-wing unmanned aerial vehicle (UAV) models is revealed.
Early detection of microvascular alterations in individuals with COVID-19 could prove to be a critical clinical advancement. This study's focus was to develop a method for identifying COVID-19 patients from raw PPG signals, achieved through deep learning algorithms applied to pulse oximeter data. To refine the methodology, we employed a finger pulse oximeter to obtain PPG signals from 93 COVID-19 patients and 90 healthy controls. For the purpose of extracting high-quality signal segments, a template-matching method was created, which filters out samples affected by noise or motion artifacts. These samples were subsequently employed in the design and construction of a customized convolutional neural network. The model's input consists of PPG signal segments, subsequently used to perform a binary classification, differentiating between COVID-19 and control cases.