Limited Computational Resources Research Articles

The Analysis of Permeability Influence on CO2 Plume based on Reservoir Simulation

Abstract Permeability is critical in site characterization and geoscience studies for carbon capture and storage projects. There is significant uncertainty of CO2 movement in the subsurface caused by permeability. This study aims to analyze the influence of permeability on the movement of CO2 plumes using a synthetic single-porosity model based on reservoir simulation. By simplifying geomechanics, employing tornado experimental design, as well as utilizing Pearson and Spearman correlations from 30,000 neural network proxy models based on Latin Hypercube and Monte Carlo experimental design, this research successfully explains the impact of permeability on CO2 movement. The reservoir can withstand pressures up to 6500 psi without rock failure. The total CO2 injection with a 90% probability is ∼1.3 TSCF equivalent, with an injection rate of 30 MMSCFD, optimized for a duration of 120 years. Initially, the CO2 movement tends to be upward during injection due to gravitational effects, but over time, it tends to move laterally. Vertical permeability exhibits a negative correlation with total CO2 injection, lateral distribution of CO2 plumes, and CO2 solubility in water. On the other hand, horizontal permeability shows a positive correlation with total CO2 injection. Factors such as perforation location and layer thickness also influence the extent of permeability’s role. However, the lateral and vertical movement of CO2 in this study has not been fully identified, and more complex quantification formulas are needed in commercial simulators. Despite the limitations in data and computational resources, this research successfully explains the movement of CO2 plumes and highlights the importance of other factors such as porosity, perforation location, layer thickness, among others. The recommendations of this study include the use of more complex quantification formulas to model the movement of CO2 plumes, along with increasing the complexity of the model.

Design of an optimized sensor fault identification within the limited computing, memory, and energy capabilities

The main goal of this work is to design an optimized sensor-fault identification and diagnostic system for the Internet of Things (IoT) and Cyber-Physical Systems (CPS). The challenge is to accomplish this task within the sensors’ limited computing, memory, and energy capabilities. More importantly, identifying errors is time-sensitive, even though the diagnosis does not have to be made quickly. This project aims to provide an enhanced sensor-fault detection and diagnostic system for the IoT and CPS with constrained energy, memory, and computation resources. The system’s goals are to promptly detect defects, lessen the computing burden on sensors, and enhance the recall and accuracy of fault detection. This study used a hybrid approach that combined principal component analysis, autoencoder, and gated recurrent unit to create an optimal sensor-fault detection and diagnostic system. There were 1001 sensor readings in the dataset; 112 were defective, while the remaining 888 were normal. The investigation showed that the suggested method, which detected faults with an accuracy of 95% and recall of 92%, achieved high accuracy and recall in recognizing defects in IoT and CPS. With significantly shorter processing times, the system’s potential to reduce computational strain on sensors was also proved. The findings of this study indicate that the suggested optimized sensor-fault detection and diagnosis system successfully detects faults in IoT and CPS with limited computation, memory, and energy resources. The system’s ability to reduce the computing burden on sensors while improving accuracy and recall makes it an appealing choice for industrial and commercial applications.

Development of Dispersion Calculator SAFEDC of Guided Wave with Complex Material and Waveguide

The propagation characteristics of guided waves (GW) are related to both the material, loading and geometry parameters. Correct understanding of GW is the key step towards further GW-based structural health monitoring. A dispersion calculator of GW propagating in the complex geometry with viscoelastic and anisotropic material parameter in the open waveguide is developed based on the semi-analytical finite element (SAFE) method and perfectly matched layer (PML). By assuming the simple harmonic motion along the propagation direction and adopting the mesh discretization in the cross section, SAFE features the capability of GW calculation in the complex waveguide at only a cost of limited calculation resources. Hence, besides the capabilities of the most off-the-shelf tools focusing on the analytical solution of GW in simple plate or pipeline, the developed algorithm further offers the solution of GW in open waveguide with laminates of arbitrary ply stacking sequences and in arbitrarily complex cross section. Quadratic one-dimensional and triangular elements are adopted to automatically discretize the plate structure and complex cross section, respectively. The obtained results of GW in plates are in a good agreement with the results obtained with the off-the shelf-tools that adopt analytical derivations, and show stronger robustness of mode searching and sorting by avoiding the complex root-finding procedure required in the analytical derivation. The most GW features, including phase velocity, group velocity, attenuation, wave structure in terms of displacement, stress, and strain, and animation of wave propagation are directly calculated. All these functions are integrated and updated in the previously developed freeware SAFEDC, whose 1st version SAFEDC v1.0 was published in September 2023.

Composite structures with embedded fiber optic sensors: A smart propellant tank for future spacecraft applications

Modern spacecraft and launch vehicle design is more oriented towards reducing system-level design and assembly complexities. In order to maintain high overall system performance while reducing these complexities, the use of smart materials and smart structural components is a well-known practice and is currently of rising interest to space systems' designers. The paper discusses a concept of smart space structures, in particular, a carbon fiber composites structure embedded with Optical Fiber Sensors (OFS) for spacecraft and launch vehicle applications. This study highlights the operational requirements for such tank and the smart features enabled by the optical fiber sensors. For the latter aspect, a quantitative comparison between Fiber Bragg Grating sensors (FBGs) and Distributed Optical Fiber Sensors (DOFS) based on Optical Frequency Domain Reflectometry (OFDR) is presented to state their core performance parameters, such as the sensitivity, sensing range, dynamic measurement capability, and spatial resolution. The increased performance and reliability in harsh environments associated with fiber optic sensors come with a reduction in size, mass, and power consumption compared to the conventional electronic sensors. Optical fiber sensors embedded in carbon fiber structures have proven their capability in providing accurate real-time measurements of temperature and monitoring structural integrity while detecting precisely possible points of rupture and failure as discussed and demonstrated in the literature review. The applications of fiber optic sensing in smart propellant tanks may extend to detecting fluid leakage, also providing increased precision in propellant gauging through temperature mapping, and can be used in on-ground qualification, pre-flight testing, as well as in-orbit operation, condition, and structural health monitoring. The article presents a statement for an optimal FOS embedding approach in composite pressure vessels and discusses the related placement and orientation method for the fiber optic sensors, coupled with a one component simplified analytical stress-strain transfer model deriving the stress component along the maximum principal direction (i.e., σMaxPrincipal). The novel approach is believed to serve the optimal employment of embedded FOS in composite structures, e.g., pressure vessels and light-weight structures in spacecraft, among other applications. The simplified model is believed to pave the way for effective data interpretation and processing, utilizing the available limited computational resources on-board the spacecraft.

Search for high-probability differential characteristics of the lightweight block cipher algorithm present with non-standard substitution blocks

The development of the Internet of Things and the associated devices has made it necessary to establish and implement encryption standards to ensure secure data transmission. These standards need to be comply with fundamental encryption principles and cater to devices with limited computational resources. As a result, lightweight cryptography has emerged as a distinct field within cryptography. The PRESENT block cipher algorithm is a lightweight encryption algorithm designed for deployment in resource-constrained devices. It requires comprehensive and ongoing vulnerability analysis against both known and novel cryptanalysis methods. This work extensively investigates the PRESENT block cipher algorithm, examining its components, operational principles, and key scheduling algorithm. This study analyses existing research on the algorithm with regards to contemporary cryptanalysis methods. Differential cryptanalysis was selected as the method of choice. The requirements for constructing S-boxes, as set forth by the algorithm developers, are reviewed. Two alternative S-boxes are formulated and presented based on these requirements. The paper presents a methodology for identifying high-probability differential characteristics for the PRESENT algorithm, using a substitute substitution block that differs from the one proposed by the developers. The research reports on the encryption algorithm PRESENT, using alternative substitution blocks, and evaluates its resistance to differential cryptanalysis. The text presents the results of applying the methodology for searching differential characteristics to the substituted blocks in the PRESENT algorithm. A comparative analysis is made between the results obtained through the differential characteristic search methodology for the PRESENT algorithm with alternative substitution blocks and the known results for this algorithm.

DNN acceleration in vehicle edge computing with mobility-awareness: A synergistic vehicle–edge and edge–edge framework

In recent years, vehicular networks have seen a proliferation of applications and services such as image tagging, lane detection, and speech recognition. Many of these applications rely on Deep Neural Networks (DNNs) and demand low-latency computation. To meet these requirements, Vehicular Edge Computing (VEC) has been introduced to augment the abundant computation capacity of vehicular networks to complement limited computation resources on vehicles. Nevertheless, offloading DNN tasks to MEC (Multi-access Edge Computing) servers effectively and efficiently remains a challenging topic due to the dynamic nature of vehicular mobility and varying loads on the servers. In this paper, we propose a novel and efficient distributed DNN Partitioning And Offloading (DPAO), leveraging the mobility of vehicles and the synergy between vehicle–edge and edge–edge computing. We exploit the variations in both computation time and output data size across different layers of DNN to make optimized decisions for accelerating DNN computations while reducing the transmission time of intermediate data. In the meantime, we dynamically partition and offload tasks between MEC servers based on their load differences. We have conducted extensive simulations and testbed experiments to demonstrate the effectiveness of DPAO. The evaluation results show that, compared to offloaded all tasks to MEC server, DPAO reduces the latency of DNN tasks by 2.4x. DPAO with queue reservation can further reduce the task average completion time by 10%.

ARLP: Automatic multi-agent transformer reinforcement learning pruner for one-shot neural network pruning

Overparameterized Neural Networks demonstrate state-of-the-art performance; however, the escalating demand for more compact and energy-efficient neural networks has arisen to facilitate the deployment of machine learning applications on devices with limited computational resources. A prevalent approach employs various pruning techniques. However, hyperparameters, such as the pruning ratio for each layer in pruning techniques, are typically set by human experts and often lack optimization. In this paper, we therefore propose a novel method named “Automatic Multi-Agent Transformer Reinforcement Learning Pruner” (ARLP). ARLP leverages a transformer-based multi-agent reinforcement learning controller to autonomously prune networks at initialization by extracting network meta-features. This autonomous process eliminates the need for human intervention in determining the optimal pruning ratio for each layer. The meta-features are derived from various zero-cost pruning-at-initialization proxies to perform One-shot Pruning. Extensive experimental results demonstrate that ARLP outperforms other state-of-the-art methods, establishing its efficacy in achieving superior performance.

Democratizing protein language models with parameter-efficient fine-tuning

Proteomics has been revolutionized by large protein language models (PLMs), which learn unsupervised representations from large corpora of sequences. These models are typically fine-tuned in a supervised setting to adapt the model to specific downstream tasks. However, the computational and memory footprint of fine-tuning (FT) large PLMs presents a barrier for many research groups with limited computational resources. Natural language processing has seen a similar explosion in the size of models, where these challenges have been addressed by methods for parameter-efficient fine-tuning (PEFT). In this work, we introduce this paradigm to proteomics through leveraging the parameter-efficient method LoRA and training new models for two important tasks: predicting protein-protein interactions (PPIs) and predicting the symmetry of homooligomer quaternary structures. We show that these approaches are competitive with traditional FT while requiring reduced memory and substantially fewer parameters. We additionally show that for the PPI prediction task, training only the classification head also remains competitive with full FT, using five orders of magnitude fewer parameters, and that each of these methods outperform state-of-the-art PPI prediction methods with substantially reduced compute. We further perform a comprehensive evaluation of the hyperparameter space, demonstrate that PEFT of PLMs is robust to variations in these hyperparameters, and elucidate where best practices for PEFT in proteomics differ from those in natural language processing. All our model adaptation and evaluation code is available open-source at https://github.com/microsoft/peft_proteomics. Thus, we provide a blueprint to democratize the power of PLM adaptation to groups with limited computational resources.

Prior-free 3D tracking of a fast-moving object at 6667 frames per second with single-pixel detectors.

Real-time tracking and 3D trajectory computation of fast-moving objects is a promising technology, especially in the field of autonomous driving. However, existing image-based tracking methods face significant challenges when it comes to real-time tracking, primarily due to the limitation of storage space and computational resources. Here, we propose a novel approach that enables real-time 3D tracking of a fast-moving object without any prior motion information and at a very low computational cost. To enable 3D coordinate synthesis with a space-efficient optical setup, geometric moment patterns are projected on two non-orthogonal planes with a spatial resolution of 125 μm. Our experiment demonstrates an impressive tracking speed of 6667 frames per second (FPS) with a 20 kHz digital micromirror device (DMD), which is more than 200 times faster than the widely adopted video-based tracking methods. To the best of our knowledge, this is the highest tracking speed record in the field of single-pixel 3D trajectory tracking. This method promotes the development of real-time tracking techniques with single-pixel imaging (SPI).

Dynamic resource management for 6G vehicular networks: CORA-6G offloading and allocation strategies

Given the burgeoning demands of real-time applications and limited on-board computational resources, the rapid proliferation of 6G-enabled vehicular networks brings forth the critical challenge of efficient computation offloading. This paper introduces the CORA-6G algorithm, a novel solution specifically designed to address this issue by determining the optimal offloading ratio. Using a robust convex optimization problem, CORA-6G maximizes the computational utility, ensuring efficient resource allocation and enhanced responsiveness. Quantitative evaluations reveal that, when compared to conventional methods, CORA-6G improves offloading efficiency by up to 25%, thereby significantly boosting the overall performance of vehicular applications. This research underscores the potential of CORA-6G as a cornerstone for future 6G vehicular network advancements.

DESIGN: Online Device Selection and Edge Association for Federated Synergy Learning-enabled AIoT

The Artificial Intelligence of Things (AIoT) is an emerging technology that enables numerous AIoT devices to participate in big data analytics and machine learning (ML) model training, providing various customized intelligent services for industry manufacturing. Federated Learning (FL) empowers AIoT applications with privacy-preserving distributed model training without sharing raw data. However, due to IoT devices’ limited computing and memory resources, existing FL approaches for AIoT applications cannot support efficient large-scale model training. Federated synergy learning (FSyL) is a promising collaborative paradigm that alleviates the computation and communication overhead on resource-constrained AIoT devices via offloading part of the ML model to the edge server for end-to-edge collaborative training. Existing FSyL works neither efficiently address the inter-round device selection to improve model diversity nor determine the intra-round edge association to reduce the training cost, which hinders the applications of FSyL-enable AIoT. Motivated by this issue, this paper first investigates the bottlenecks of executing FSyL in AIoT. It builds an optimization model of joint inter-round device selection and intra-round edge association for balancing model diversity and training cost. To tackle the intractable coupling problem, we present a framework named Online DE vice S elect I on and Ed G e Associatio N for Cost-Diversity Trade-offs FSyL (DESIGN). First, the edge association subproblem is extracted from the original problem, and game theory determines the optimal association decision for an arbitrary device selection. Then, based on the optimal association decision, device selection is modeled as a combinatorial multi-armed bandit (CMAB) problem. Finally, we propose an online mechanism to obtain joint device selection and edge association decisions. The performance of DESIGN is theoretically analyzed and experimentally evaluated on real-world datasets. The results show that DESIGN can achieve up to \(84.3\%\) in cost-saving with an accuracy improvement of \(23.6\%\) compared with the state-of-the-art.

An improved problem transformation algorithm for large-scale multi-objective optimization

Solving Large-Scale Multi-Objective Optimization Problems (LSMOPs) is the major challenge in evolution computation. Due to the large number of decision variables involved, it is not easy for existing multi-objective evolutionary algorithms to search the entire decision variable space with limited computation resources. To address the above problem, a large-scale multi-objective evolutionary algorithm based on problem transformation, called LSMOEA/PT, is presented in this paper. LSMOEA/PT uses an improved problem transformation strategy to transform LSMOPs into small-scale multi-objective problems to accelerate convergence speed and reduce computational resources. Specifically, The transformation strategy selects reference solutions to initialize a weight population by applying the opposite individual method. After optimizing the weight population, a dual line strategy is implemented to combine the weight vectors with the decision variables from the original population, thereby generating a new population. The new population is combined with the original population for environment selection. In LSMOEA/PT, a double learning swarm optimizer is introduced to speed up the convergence of the population. A uniform distribution strategy is conducted to update transformed solutions after the problem transformation process, increasing the diversity of the population. Experimental results show that LSMOEA/PT is competitive with eleven state-of-the-art algorithms on large-scale multi-objective optimization test problems with up to 2000 decision variables.

Laplace-domain crosstalk-free source-encoded elastic full-waveform inversion using time-domain solvers

Crosstalk-free source-encoded elastic full-waveform inversion (FWI) using time-domain solvers demonstrates skill and efficiency at conducting seismic inversions involving multiple sources and receivers with limited computational resources. A drawback of common formulations of the procedure is that, by sweeping through the frequency domain randomly at a rate of one or a few sparsely sampled frequencies per shot, it is difficult to simultaneously incorporate time-selective data windows, as necessary for the targeting of arrivals or wave packets during the various stages of the inversion. Here, we solve this problem by using the Laplace transform of the data. Using complex-valued frequencies allows for damping the records with flexible decay rates and temporal offsets that target specific traveltimes. We present the theory of crosstalk-free source-encoded FWI in the Laplace domain, develop the details of its implementation, and illustrate the procedure with numerical examples relevant to exploration-scale scenarios.

EUAVDet: An Efficient and Lightweight Object Detector for UAV Aerial Images with an Edge-Based Computing Platform

Crafting an edge-based real-time object detector for unmanned aerial vehicle (UAV) aerial images is challenging because of the limited computational resources and the small size of detected objects. Existing lightweight object detectors often prioritize speed over detecting extremely small targets. To better balance this trade-off, this paper proposes an efficient and low-complexity object detector for edge computing platforms deployed on UAVs, termed EUAVDet (Edge-based UAV Object Detector). Specifically, an efficient feature downsampling module and a novel multi-kernel aggregation block are first introduced into the backbone network to retain more feature details and capture richer spatial information. Subsequently, an improved feature pyramid network with a faster ghost module is incorporated into the neck network to fuse multi-scale features with fewer parameters. Experimental evaluations on the VisDrone, SeaDronesSeeV2, and UAVDT datasets demonstrate the effectiveness and plug-and-play capability of our proposed modules. Compared with the state-of-the-art YOLOv8 detector, the proposed EUAVDet achieves better performance in nearly all the metrics, including parameters, FLOPs, mAP, and FPS. The smallest version of EUAVDet (EUAVDet-n) contains only 1.34 M parameters and achieves over 20 fps on the Jetson Nano. Our algorithm strikes a better balance between detection accuracy and inference speed, making it suitable for edge-based UAV applications.

An adaptive simulated annealing-based computational offloading scheme in UAV-assisted MEC networks

Mobile edge computing (MEC) is an advanced technology that enables fifth-generation (5G) applications. It uses a mechanism to offload computation from mobile devices (MDs) to edge servers at the access point (AP) to improve the quality of computation. By leveraging the strengths of unmanned aerial vehicles (UAVs), we can install edge servers on UAVs to support task offloading. However, this is constrained by the limited computational resources of UAVs and high energy consumption. Due to the complexity of such architecture, the joint optimization of energy consumption and delay by applying classical optimization algorithms cannot work well either. So far, there is no research work that addresses the problem of computation offloading in UAV-based MEC networks considering the UAV-AP-MD architecture that is well applicable in 5G scenarios; this motivates us to propose this work. We first formulate this problem as an optimization problem and then develop an adaptive simulated annealing-based method for joint optimization of energy consumption and delay. We design a mechanism to dynamically increase the cooling rate as a function of decreasing temperature to achieve better convergence, and implement a task queue for MDs and servers. Finally, we perform numerical simulations and find that the proposed method reduces the energy consumption (17%) and delay (50%) of MDs and UAV while it has the best task dropped rate (50%) and fairness (4.2%) compared to other known methods.

ESC-NAS: Environment Sound Classification Using Hardware-Aware Neural Architecture Search for the Edge

The combination of deep-learning and IoT plays a significant role in modern smart solutions, providing the capability of handling task-specific real-time offline operations with improved accuracy and minimised resource consumption. This study provides a novel hardware-aware neural architecture search approach called ESC-NAS, to design and develop deep convolutional neural network architectures specifically tailored for handling raw audio inputs in environmental sound classification applications under limited computational resources. The ESC-NAS process consists of a novel cell-based neural architecture search space built with 2D convolution, batch normalization, and max pooling layers, and capable of extracting features from raw audio. A black-box Bayesian optimization search strategy explores the search space and the resulting model architectures are evaluated through hardware simulation. The models obtained from the ESC-NAS process achieved the optimal trade-off between model performance and resource consumption compared to the existing literature. The ESC-NAS models achieved accuracies of 85.78%, 81.25%, 96.25%, and 81.0% for the FSC22, UrbanSound8K, ESC-10, and ESC-50 datasets, respectively, with optimal model sizes and parameter counts for edge deployment.

An adaptive strategy based multi-population multi-objective optimization algorithm

An algorithm is sensitive to parameters; different parameter settings for solving optimization problems can thus have a serious impact on algorithm performance. This leads to an inability to determine the optimal set of parameters for the algorithm to solve the problem at hand. In this study, we propose an adaptive strategy with a multi-population multi-objective algorithm (A-MPMO) framework to select the appropriate set of genetic settings according to the problem to be solved and eliminate the sensitivity of the algorithm to the parameters. Multi-population are often combined with Evolutionary Algorithms (EAs) as an effective strategy to maintain population diversity. First, we divided the population generated by the algorithm into multiple subpopulations to expand the search range and updated them iteratively using operators with different genetic parameters. Second, based on multi-population, subpopulations compete for limited computational resources, implying that the size of each subpopulation adaptively adjusts according to the degree of its contribution to problem solving. Finally, a set of subpopulations that are best suited to solve the problem is selected to improve the adaptability to different problems. For DTLZ, ZDT, and UF, compared to the other algorithms, A-MPMO was experimentally shown to produce better performance.

A Novel Attention‐Based Layer Pruning Approach for Low‐Complexity Convolutional Neural Networks

Deep learning (DL) has been very successful for classifying images, detecting targets, and segmenting regions in high‐resolution images such as whole slide histopathology images. However, analysis of such high‐resolution images requires very high DL complexity. Several AI optimization techniques have been recently proposed that aim at reducing the complexity of deep neural networks and hence expedite their execution and eventually allow the use of low‐power, low‐cost computing devices with limited computation and memory resources. These methods include parameter pruning and sharing, quantization, knowledge distillation, low‐rank approximation, and resource efficient architectures. Rather than pruning network structures including filters, layers, and blocks of layers based on a manual selection of a significance metric such as l1‐norm and l2‐norm of the filter kernels, novel highly efficient AI‐driven DL optimization algorithms using variations of the squeeze and excitation in order to prune filters and layers of deep models such as VGG‐16 as well as eliminate filters and blocks of residual networks such as ResNet‐56 are introduced. The proposed techniques achieve significantly higher reduction in the number of learning parameters, the number of floating point operations, and memory space as compared to the‐state‐of‐the‐art methods.

Implementation of Lightweight Machine Learning-Based Intrusion Detection System on IoT Devices of Smart Homes

Smart home devices, also known as IoT devices, provide significant convenience; however, they also present opportunities for attackers to jeopardize homeowners’ security and privacy. Securing these IoT devices is a formidable challenge because of their limited computational resources. Machine learning-based intrusion detection systems (IDSs) have been implemented on the edge and the cloud; however, IDSs have not been embedded in IoT devices. To address this, we propose a novel machine learning-based two-layered IDS for smart home IoT devices, enhancing accuracy and computational efficiency. The first layer of the proposed IDS is deployed on a microcontroller-based smart thermostat, which uploads the data to a website hosted on a cloud server. The second layer of the IDS is deployed on the cloud side for classification of attacks. The proposed IDS can detect the threats with an accuracy of 99.50% at cloud level (multiclassification). For real-time testing, we implemented the Raspberry Pi 4-based adversary to generate a dataset for man-in-the-middle (MITM) and denial of service (DoS) attacks on smart thermostats. The results show that the XGBoost-based IDS detects MITM and DoS attacks in 3.51 ms on a smart thermostat with an accuracy of 97.59%.

SpanEffiDet: Span-Scale and Span-Path Feature Fusion for Object Detection

Lower versions of EfficientDet (such as D0, D1) have smaller network structures and parameter sizes, but lower detection accuracy. Higher versions exhibit higher accuracy, but the increase in network complexity poses challenges for real-time processing and hardware requirements. To meet the higher accuracy requirements under limited computational resources, this paper introduces SpanEffiDet based on the channel adaptive frequency filter (CAFF) and the Span-Path Bidirectional Feature Pyramid structure. Firstly, the CAFF module proposed in this paper realizes the frequency domain transformation of channel information through Fourier transform and effectively extracts the key features through semantic adaptive frequency filtering, thus, eliminating channel redundant information of EfficientNet. Simultaneously, the module has the ability to compute the weights across the channels and at fine granularity, and capture the detailed information of element features. Secondly, a two-way characteristic pyramid network multi-level cross-BIFPN, which can achieve multi-layer and multi-nodes, is proposed to build cross-level information transmission to incorporate both semantic and positional information of the target. This design enables the network to more effectively detect objects with significant size differences in complex environments. Finally, by introducing generalized focal Loss V2, reliable localization quality estimation scores are predicted from the distribution statistics of bounding boxes, thereby improving localization accuracy. The experimental results indicate that on the MS COCO dataset, SpanEffiDet-D0 achieved an AP improvement of 3.3% compared to the original EfficientDet series algorithms. Similarly, on the PASCAL VOC2007 and 2012 datasets, the mAP of SpanEffiDet-D0 is respectively 1.66 and 2.65% higher than that of EfficientDet-D0.