Complexity Of Algorithm Research Articles

QPSK circuit optimization Based on Model-Based Design

In order to address the difficult problem of hardware implementation of complex communication algorithm, this paper takes digital phase modulation as an example, and uses model-based design approach to quickly complete the algorithm modeling, simulation and automatic generation of HDL code. Model-based design is a fast and effective design process for algorithm modeling, simulation, testing and automatic code generation. The simulation results show that the model-based design method cannot only quickly accomplish the modeling and testing of the algorithm, but also improve the design accuracy.

A highly efficient tunnel lining crack detection model based on Mini-Unet.

The accurate detection of tunnel lining cracks and prompt identification of their primary causes are critical for maintaining tunnel availability. The advancement of deep learning, particularly in the domain of convolutional neural network (CNN) for image segmentation, has made tunnel lining crack detection more feasible. However, the CNN-based technique for tunnel lining crack detection commonly prioritizes increasing algorithmic complexity to enhance detection accuracy, posing a challenge in balancing the accuracy of detection and the efficiency of the algorithm. Motivated by the superior performance of Unet in image segmentation, this paper proposes a lightweight tunnel lining crack detection model named Mini-Unet, which refined the Unet architecture and utilized depthwise separable convolutions (DSConv) to replace some standard convolution layers. In the optimization of the proposed model parameters, applying a hybrid loss function that integrated dice loss and cross-entropy loss effectively tackled the imbalance between crack and background categories. Several models were set up to contrast with Mini-Unet and the experimental results were analyzed. Mini-Unet achieves a mean intersection over union (MIoU) of 60.76%, a mean precision of 84.18%, and a frame per second (FPS) of 5.635, respectively. Mini-Unet outperforms several mainstream models, enabling rapid detection while maintaining identified accuracy and facilitating the practical application of AI power for real-time tunnel lining crack detection.

Comparative analysis of direct search methods for material design optimization

This paper contributes to the field of optimization for periodic composite material design by systematically evaluating the performance of four direct search algorithms: greedy, simulated annealing (SA), genetic algorithms (GA), and particle swarm optimization (PSO). The central goal is to determine the optimal arrangement of two material domains inside a representative volume element (RVE) while respecting the constraint that half volume of the domains is composed of a specific material, with the effective shear modulus as the objective function. The main contribution of this work is the detailed comparison of these algorithms in terms of success rates and computational efficiency, providing critical insights into their suitability for different problem scales. For a small-scale problem (16 cells), PSO achieves a 100% success rate, while SA demonstrates over 90% success but at a higher computational cost. The greedy algorithm, despite its simplicity, achieves a 72% success rate with the lowest computational effort, requiring an average of 256 objective function evaluations. GA, however, exhibits the lowest success rate at 52% and requires the highest average number of evaluations (5003.2). Furthermore, the paper extends the analysis to a more complex problem (36 cells), where the adaptability and efficiency of the algorithms are tested. This investigation highlights the trade-offs between algorithmic complexity, success rate, and computational cost, offering practical guidelines for selecting suitable optimization methods based on problem size and characteristics.

Robust double encryption and watermarking algorithms for color watermark images

Abstract Current digital watermarking technologies mainly focus on the imperceptibility and robustness of watermark embedding, while the security of watermarking images is also worth further research. Considering nonlinear characteristics and the integration structure of storage and computation, memristors can be introduced into encryption algorithms to improve the effect of encryption. The paper proposes a double encryption algorithm for color watermark images based on MCNN (Memristive Cellular Neural Networks) and Arnold transform, generates chaotic sequences for watermark image encryption by introducing memristors to the CNN (Cellular Neural Networks) to construct MCNN, scrambles the images using the Arnold transform to achieve the double encryption of pixel values and pixel positions, and enhances the security of the watermark images. Adopting the SE (Spectral Entropy) complexity algorithm optimizes the parameters of MCNN, and improves the performance of the double encryption algorithm. The embedding and extraction of the encrypted watermark image is realized by the algorithm combining CT (Contourlet Transform) and SVD (Singular Value Decomposition), which enhances the ability to resist common attacks such as compression and rotation attacks. Experiment results show the proposed algorithms can better maintain the quality of the color watermark images, break the statistical characteristics of the original images, and the generated key has good randomness. In addition, the presented algorithms are highly sensitive to the key, and improve the ability to resist statistical attacks, differential attacks, exhaustive attacks and common image attacks with good security, robustness and imperceptibility.

Monolithically Integrated Quantum Dots in a 22‐nm Fully Depleted Silicon‐on‐Insulator Process Operating at 3 K

ABSTRACTQuantum computers comprising large‐scale arrays of qubits will enable complex algorithms to be executed to provide a quantum advantage for practical applications. A prerequisite for this milestone is a power‐efficient qubit control and detection system operating at cryogenic temperatures. Implementing such systems in complementary metal‐oxide‐semiconductor (CMOS) technology offers clear advantages in terms of scalability. Here, we present a fully integrated quantum dot array in which silicon quantum wells are co‐located with control and detection circuitry on the same die in a commercial 22‐nm fully depleted silicon‐on‐insulator (FDSOI) process. Our system comprises a two‐dimensional quantum dot array, integrated with 8 detectors and 32 injectors, operating at 3 K inside a cryo‐cooler. The power consumption of the control and detection circuitry is 2.5 mW per qubit without body biasing. The design utilizes 0.8‐V nominal devices. The setup allows us to verify discrete charge injection control and detection at the quantum dot array and demonstrate the feasibility of this architecture for scaling up the existing quantum core to hundreds and thousands of physical qubits.

Ethics dumping in artificial intelligence

Artificial Intelligence (AI) systems encode not just statistical models and complex algorithms designed to process and analyze data, but also significant normative baggage. This ethical dimension, derived from the underlying code and training data, shapes the recommendations given, behaviors exhibited, and perceptions had by AI. These factors influence how AI is regulated, used, misused, and impacts end-users. The multifaceted nature of AI’s influence has sparked extensive discussions across disciplines like Science and Technology Studies (STS), Ethical, Legal and Social Implications (ELSI) studies, public policy analysis, and responsible innovation—underscoring the need to examine AI’s ethical ramifications. While the initial wave of AI ethics focused on articulating principles and guidelines, recent scholarship increasingly emphasizes the practical implementation of ethical principles, regulatory oversight, and mitigating unforeseen negative consequences. Drawing from the concept of “ethics dumping” in research ethics, this paper argues that practices surrounding AI development and deployment can, unduly and in a very concerning way, offload ethical responsibilities from developers and regulators to ill-equipped users and host environments. Four key trends illustrating such ethics dumping are identified: (1) AI developers embedding ethics through coded value assumptions, (2) AI ethics guidelines promoting broad or unactionable principles disconnected from local contexts, (3) institutions implementing AI systems without evaluating ethical implications, and (4) decision-makers enacting ethical governance frameworks disconnected from practice. Mitigating AI ethics dumping requires empowering users, fostering stakeholder engagement in norm-setting, harmonizing ethical guidelines while allowing flexibility for local variation, and establishing clear accountability mechanisms across the AI ecosystem.

Full-color electrophoretic display with high halftone image quality, reduced power, and deep-learning-enabled fast implementation

The electrophoretic display (EPD) with low power consumption, good sunlight readability, and flexibility is ideal for the Internet of Things. However, color EPDs have very limited grayscales (typically 1-bit) because it is challenging to precisely control the electrophoretic particles of multiple colors. Thus, halftone is usually employed to achieve continuous-tonal full-color EPDs alternatively. Nevertheless, halftone achieves continuous tones at the expense of a significant increase in driving current. This study first proposes and experimentally verifies a model that can accurately predict a driving current from image content. Next, a multi-objective optimized (MOO) halftone algorithm based on MOEA/D (multi-objective evolutionary algorithm based on decomposition) is proposed to consider image quality and driving current simultaneously. As a result, Pareto optimality is achieved, i.e., no image simultaneously performs better in image quality and driving current. A mean driving current reduction of 33.8% concerning the traditional error-diffusion algorithm is experimentally verified on a seven-color EPD with maintained halftone image quality. Considering the high computational complexity of the iteration-based MOO algorithm, this study also discusses the real-time generation of optimal halftone images using a generative adversarial network (GAN). Compared with the optimal halftone images slowly generated by the MOO, the GAN achieves almost the same driving current and a mean absolute error of 2.04, in terms of CIE76 color difference. The proposed algorithm enables full-color EPDs with high-quality continuous tones, reduced power consumption, and a GAN-based real-time implementation.

Implementing the Grover algorithm in homomorphic encryption schemes

We apply quantum homomorphic encryption (QHE) schemes suitable for circuits with a polynomial number of T+T† gates to Grover's algorithm, performing a simulation in Qiskit of a Grover circuit that contains three qubits. The T+T†-gate complexity of Grover's algorithm is also analyzed in order to show that any Grover circuit can be evaluated homomorphically in an efficient manner. We discuss how to apply these QHE schemes to allow for the efficient homomorphic evaluation of any Grover circuit composed of n qubits using n−2 extra ancilla qubits. We also show how the homomorphic evaluation of the special case where there is only one marked item can be implemented using an algorithm that makes the decryption process more efficient compared with the standard Grover algorithm. Published by the American Physical Society 2024

Bandwidth-aware fast inverse lithography technology using Nesterov accelerated gradient

Inverse lithography technology (ILT) is a leading-edge method for improving the resolution and image fidelity of optical lithography systems in semiconductor manufacturing. However, the massive computational complexity of ILT presents the inherent runtime bottleneck, which hinders its application to the large-scale lithography layouts. This paper innovates a fast ILT method by reducing both of the inherent algorithmic complexity and the required iteration number. Based on the diffraction-limited nature of optical lithography system, a bandwidth-aware acceleration approach is applied to reduce the calculation complexities of the forward imaging model and inverse gradient computation in each ILT iteration. In addition, a carefully designed algorithm based on the Nesterov accelerated gradient (NAG) is developed to achieve nearly quadratic convergence rate, thereby reducing the total number of iterations required to obtain the optimal solution. Numerical experiments show that the proposed method can improve the speed by hundreds of times over the traditional gradient-based ILT algorithms without sacrificing accuracy.

Artificial intelligence: basic terms and concepts, the application in healthcare and clinical medicine

Objective: to explore the potential and challenges of artificial intelligence (AI) in clinical medicine and healthcare, and to determine the prospects for its implementation to improve diagnosis, treatment, and medical data management.Material and methods. A literature review on the main terms and concepts of AI, its classification by application area, technologies, and methodologies was carried out. The learning methods such as supervised, unsupervised, and reinforcement learning were considered, as well as examples of AI application in various areas of medicine, including disease diagnosis and personalized medicine.Results. AI shows significant potential in improving diagnosis, optimizing treatment processes, and managing healthcare resources. Main application areas are related to medical image analysis, developing individualized treatment plans, and healthcare management. However, using AI faces challenges such as data availability and bias, fragmentation of systems, and complexity of algorithm interpretation.Conclusion. Despite the existing challenges, the implementation of AI in medicine has great prospects, including improved diagnostic accuracy, reduced task completion time, and development of personalized medicine. It is important to consider the ethical aspects and the demand for further study of AI application in medicine to achieve the best results.

Creep Lifetime Prediction of Alloy 617 Using Black Box Machine Learning Approach

Abstract This study explored the application of black box machine learning (ML) to build high throughput models that predict the creep response of Ni-based Alloy 617. Black box ML refers to highly complex machine learning algorithms that generate outputs from inputs without an interpretable internal process. The rapid implementation of suitable heat of alloys into targeted service is impeded by the extended qualification process involving chemistry-processing-structure-performance assessment. The ASME B&PV Code III outlines the requirement of 10,000 h of creep testing before each heat can be qualified for service and 30,000 h for heats that exhibit metastable phases. There is a critical need to shorten the development-to-deployment timeline for heats of an alloy at specific applications. Recent advancement in ML offers the ability to identify correlations in data which is difficult to elucidate by other approaches. To that end, black box ML is employed to expedite the HEAT qualification of Alloy 617 and predict performance from HEAT chemistry out to an unprecedented timescale. In this study, creep data for Ni-based Alloy 617—a solid solution strengthened material is gathered from a wide range of data sources. The alloy chemistry, phases, precipitates, and microstructural features are analyzed to obtain the key strengthening mechanism. Service conditions, mechanical properties, chemistry, and chemical ratios are provided as potential input features. The Pearson correlation coefficient with a 95% confidence bound is employed for input feature screening where poorly correlated inputs are eliminated to speed up the ML process and prevent under- and/or over-fitting of predictions. In the ML algorithm, the selected input features are regarded as predictors, and rupture is regarded as the response. An algorithm evaluation is performed where 20 ML algorithms are trained with the training set. The three-layered neural network (NN) was observed to be the best algorithm for the given data based on statistical rationale. The NN accurately predicts rupture across a range of isotherms and data sources. The interpolative and extrapolative predictions are in compliance with ECCC V5 guidelines. A post-audit validation exhibits neither under- nor over-fitting and confirms the applicability of NN algorithms to unseen data. The black box ML provides a pathway to predict the performance directly from chemistry and opens avenues to rapid heat qualification.

DEVELOPMENT OF AUTOMATED CONTROL SYSTEM AND REGISTRATION OF METAL IN CONTINUOUS CASTING

Context. Modern industrial enterprises face challenges that require introduction of latest technologies to improve efficiency and competitiveness. In metallurgy, one of key stages is continuous casting, where quality of products and economic performance of enterprise depend on accuracy and efficiency of process control. Products made using continuous casting technology are widely used in various industries due to their high mechanical properties, structural uniformity and cost-effectiveness. The development of automated metal management and registration system is becoming not only relevant, but also necessary to ensure stable and efficient production. The problem of improving quality of metal products has always been one of most important tasks in steel industry. Imperfect technological processes, human error and equipment malfunctions can lead to defects in finished metal products. This, in turn, affects final characteristics of products, their durability and reliability. To date, available sources have not yet found complete solution to this problem. Therefore, it is necessary to formulate problem and develop algorithm for operation of automated system for controlling and registering metal in continuous casting. Objective. The goal of work is to develop automated metal management and registration system to improve quality of metal products. Method. To achieve this goal, parametric model was proposed, which is formalized on basis of set theory. The model takes into account key parameters of continuous casting process: material characteristics, structural features of crystallizer, casting modes, metal level in crystallizer, and position of shot stopper. Results. The problem was formulated and key parameters were determined, which are taken into account in system’s algorithm, which made it possible to develop system for controlling parameters of continuous casting to solve problem of improving quality of metal products. Conclusions. To improve quality of metal products and stability of casting process, parametric model was created that is comprehensive, allows optimization of key parameters and ensures accuracy of process control by integrating not only modes of product formation, but also takes into account specific properties of source material (chemical composition of material grade, etc.) and design features of casting plant. Algorithm for automated control system has been developed that takes into account relationships between certain key parameters and ensures optimal control of casting process. Based on proposed complex parametric model and algorithm, automated control and metal registration system was created. The focus of work is on quality and efficiency of metal management and registration in continuous casting, based on modern methods of computer science and engineering. A comprehensive experimental comparison of developed system with commercial analogs in real production conditions was carried out, which allowed us to objectively assess its efficiency and reliability.

Electrocardiogram sampling frequency for optimal performance of complexity analysis and machine learning models: Discrimination between subjects with and without paroxysmal atrial fibrillation using sinus rhythm electrocardiograms

BackgroundCurrent clinical practice to diagnose atrial fibrillation (AF) requires repeated, episodic monitoring and significantly underperform in their ability to detect AF episodes. ObjectiveThere is therefore potential for artificial intelligence based methods to assist in the detection of AF. Better understanding the optimal parameters for this detection can potentially improve the sensitivity in detecting AF. MethodTen second, 12-lead electrocardiogram signals were analysed using complexity algorithms combined with machine learning techniques to predict patients who had a previously detected AF episode but had since returned to normal sinus rhythm. An investigation was performed into the impact of sampling frequency of the electrocardiogram signal on the accuracy of the machine learning models used. ResultsUsing a single complexity algorithm showed a peak accuracy of 0.69 when using signals sampled at 125Hz. In particular it was noted that improved accuracy occurred when using lead V6 compared to other available leads. ConclusionBased on these results there is potential for 12-lead electrocardiogram signals to be recorded at 125Hz as standard and used in conjunction with complexity analysis to aid in the detection of subjects with AF.

A novel ensemble machine learning exposure model system for ground-level ozone at the national scale: A case of mainland China from 2013 to 2020

In epidemiological research, accurate estimation of historical ground-level ozone (O3) concentrations with enhanced spatiotemporal resolution is crucial for effective exposure assessment. The current state-of-the-art for estimating air pollutant concentrations is a two-stage ensemble method that integrates outputs from multiple machine learning algorithms. Despite its effectiveness, opportunities exist to refine this approach for more precise O3 estimation. In this study, we propose an enhanced ensemble method that incorporates four key strategies. First, we employ high-resolution spatiotemporal predictors derived from prior machine learning studies for refined secondary learning. Second, we use sophisticated algorithms, including categorical gradient boosting, deep neural network, random forest, stochastic variable Gaussian process, transformer, and a combination of convolutional neural network and long short-term memory neural network, as sublearners to enhance learning capabilities. Third, we spatiotemporally split the sample set and then train submodels separately on each subset to eliminate the unobserved spatiotemporal heterogeneity. Finally, we apply a complex machine learning algorithm, rather than the generalized additive model, for integrating sublearner predictions, enabling the capture of intricate nonlinear relationships beyond basic spatiotemporal linear weights. To validate these improvements, we estimated daily maximum 8-h moving average O3 concentrations ([O3]MDA8) across Chinese mainland from 2013 to 2020 at a 1 km spatial resolution. The proposed method demonstrated notable accuracy, achieving an out-of-station determination coefficient (R2) of 0.943 and a root-mean-square error (RMSE) of 10.197 μg/m3. This performance marks a nearly 15% improvement over the best existing Chinese O3 exposure model based on a single algorithm and also surpasses previous studies utilizing traditional ensemble methods for other air pollutants. Our enhanced ensemble approach significantly bolsters the reliability and robustness of future environmental epidemiological studies by further mitigating “misclassification” errors.

NARA: Network-Aware Resource Allocation mechanism for minimizing quality-of-service impact while dealing with energy consumption in volunteer networks

A large-scale volunteer computing system is a type of distributed system in which contributors volunteer their computing resources, such as personal computers or mobile devices, to contribute to a larger computing effort. Volunteer resources are connected over the Internet and together form a powerful computing system capable of providing a service without depending on a service provider. Volunteer network resource allocation is the process of assigning computing tasks or services to a network of volunteer resources. The allocation process includes identifying the needed resources, selecting appropriate volunteers, and assigning tasks or services based on their capabilities. Volunteer computing systems consist of a large number of heterogeneous resources - in terms of processing power, storage, and availability - belonging to different authorities - users or organizations - and exhibiting uncertain behavior in terms of connection, disconnection, capacity, and failure. All of this makes resource allocation a challenging task in terms of ensuring a minimum quality of service, requiring complex algorithms and optimization techniques to ensure that services are efficiently allocated while respecting the constraints of the available resource. This paper introduces the Network-Aware Resource Allocation mechanism, which leverages the location, connectivity, and network latency of volunteer nodes to minimize the time a service runs with degraded quality of service and aims to deal with the energy consumption resulting from data replication requirements. This resource allocation mechanism applies to both the initial deployment of the service in the network and to the reallocation of nodes in the event that one of the allocated nodes fails or becomes unavailable. Our method has been validated in a simulation environment of a realistic volunteer system. The analysis of the results shows how our mechanism meets the quality requirements of users while minimizing the synchronization and replication times of service data replicas, as well as the time that services run with degraded quality of service, reducing the times by more than 70% in the service deployment phase and by more than 60% in the service execution phase. It also helps to reduce overall energy consumption.

PV-OSIMr: A Lowest Order Complexity Algorithm for Computing the Delassus Matrix

PV-OSIMr: A Lowest Order Complexity Algorithm for Computing the Delassus Matrix

Выбор критериев для сокращения неизвестных в методе Ньютона–Рафсона в задаче нахождения равновесного состава железной руды и топлива

Finding the equilibrium composition of a multicomponent system is required in various industries associated with chemical reactions. Reduction of the unknowns in the original nonlinear equation is possible if most of the unknowns receive values equal to zero as a result of the solution. This situation is typical to solve the problem of finding an equilibrium composition of iron ore and fuel, where the unknowns are the number of moles of all possible reaction products from given simple substances. The dependence of algorithmic complexity on the number of unknowns to solve a SLAEs is expressed through the function O(N3). Therefore, reduction of the unknowns will speed up the computational process. Thus, the solution to the problem of selecting criteria to reduce the number of the unknowns at each iteration of the Newton-Raphson method as applied to the problem of finding the equilibrium composition of iron ore and fuel is relevant. The matrix dimensionality reduction has been achieved using four strategies. The first three strategies involve finding a minimum threshold (removing values that most quickly tend to zero), after which the number of unknowns will decrease. The fourth strategy is associated with reducing the dimension of the original matrix. The results of numerical experiments to reduce the unknowns and speed up calculations are presented. Criteria have been found under which the calculation speed increases by 2–4 times and variables whose value is not equal to zero are not reduced. The criteria to reduce the unknowns implemented in the T-Energу software package make it possible to solve the problem of finding the equilibrium composition of iron ore and fuel 2–4,2 times faster. In this case, the obtained solution for the components of the equilibrium composition with the reduction of the unknowns corresponds to the complete solution with an accuracy of 10–3. Due to acceleration at the same time, it is possible to construct equilibrium compositions over a temperature range with a temperature step that is 4 times smaller.

Implementation of a non-standard system for simplifying Peirce-Webb functions

The object of research are models of optimal logic circuits based on universal Peirce-Webb functions. The problem solved is the efficiency of the technique for simplifying the Peirce-Webb functions. The extension of the non-standard system to the simplification of Peirce-Webb functions makes it possible to discover new rules of equivalent transformations of Boolean functions, and to complete the simplification procedure in one step. A feature of the simplification of functions in the Peirce-Webb basis by a non-standard system is fixing the digital project at the level of abstraction, followed by the application of the mechanism of logical synthesis to generate the corresponding equivalent at the level of gates of the logic circuit. The result of the transformation of the terms of the binary matrix in the end is some combinatorial system, metadata that can explain other data, for example, determine the minimum function for another logical basis. The interpretation of the result consists in the use of combinatorial properties of binary structures of functions in the Peirce-Webb basis and binary structures of functions in the basic basis. These properties do not depend on the selected logical basis, which makes it possible to carry out equivalent transformations on binary matrices of Peirce-Webb functions according to the rules of the algebra of the main basis. It has been experimentally confirmed that a non-standard system enables: – to reduce the algorithmic complexity of simplifying the Peirce-Webb functions; – to increase the performance of the simplification of Peirce-Webb functions by 200–300 %; – to demonstrate the visibility of the process of simplifying functions. In terms of application, the non-standard system of simplifying the Peirce-Webb functions could ensure the transfer of innovations to material production: from conducting fundamental research, expanding the capabilities of digital component design technology to organizing serial or mass production of novelties.

Leveraging the Hardware Resources to Accelerate cryo-EM Reconstruction of RELION on the New Sunway Supercomputer

The fast development of biomolecular structure determination has enabled the fine-grained study of objects in the micro-world, such as proteins and RNAs. The world is benefited. However, as the computational algorithms are constantly developed, the enrichment of features increases the algorithmic complexity and brings more computationally unfriendly modules. It calls for efficient solutions to leverage the rich and various hardware resources from the world’s most state-of-the-art supercomputing systems, and to fully accelerate the performance of the applications. In this paper, we present our efforts on porting and optimizing the 3D reconstruction of RELION, one of the most popular cryo-EM software for biomolecular structure determinations, by leveraging different resources of the latest generation of Sunway heterogeneous supercomputer. Several novel approaches are proposed to resolve different challenges faced by the complex algorithm, including a multi-level parallel scheme and operator optimizations to smartly map and scale RELION, efficient strategies to largely address the memory bottlenecks and improve data locality, lock-free writing solutions to minimize write-write conflicts, and pipelining approaches to obtain excellent computation and communication overlap. Combining all proposed optimizations, the computation time is greatly reduced to under 2 hours, achieving 11.9 × and 8.9 × speedups on two different datasets. The overall design scales to 131,072 cores, increasing parallel efficiency from 33% to 61% and from 46% to 70%, respectively. To the best of our knowledge, this is the first work that fully optimized and scaled the 3D reconstruction of RELION using the latest Sunway system.

Efficient Vehicle Detection and Optimization in Multi-Graph Mode Considering Multi-Section Tracking Based on Geographic Similarity

Vehicle detection is an important part of modern intelligent transportation systems. At present, complex deep learning algorithms are often used for vehicle detection and tracking, but high-precision detection results are often obtained at the cost of time, and the existing research rarely considers optimization algorithms for vehicle information. Based on this, we propose an efficient method for vehicle detection in multi-graph mode and optimization method considering multi-section tracking based on geographic similarity. In this framework, we design a vehicle extraction method based on multi-graph mode and a vehicle detection technology based on traffic flow characteristics, which can cope with the challenge of vehicle detection under an unstable environment. Further, a multi-section tracking optimization technology based on geographic similarity at a high video frame rate is proposed, which can efficiently identify lane change behavior and match, track, and optimize vehicles. Experiments are carried out on several road sections, and the model performance and optimization effect are analyzed. The experimental results show that the vehicle detection and optimization algorithm proposed in this paper has the best effect and high detection accuracy and robustness. The average results of Recall, Precision, and F1 are 0.9715, 0.979, and 0.9752, respectively, all of which are above 0.97, showing certain competitiveness in the field of vehicle detection.