-
21
Sparse Adaptive Optimization Based on Low Rank Decomposition for Image Defect Detection
Published 2025-01-01Subjects: “…Low rank decomposition…”
Get full text
Article -
22
Identification of Low‐Value Defects in Infrared Images of Porcelain Insulators Based on STCE‐YOLO Algorithm
Published 2025-07-01“…To solve the above problems, this paper optimizes the small target and complex environment problems in the low‐value defect recognition of insulator infrared images, and proposes the STCE‐YOLO algorithm: based on YOLOv8, the deformable large kernel attention is used to improve the detection ability of small targets; then the cross‐modal contextual feature module is applied to Integrate the features of different scales to reduce the computation of the model. …”
Get full text
Article -
23
The "Low Slow and Small" UAV target detection and tracking algorithm based on improved YOLOv7 and DeepSort
Published 2025-02-01“…To improve the accuracy of Low altitude unmanned aerial vehicle(UAV) target detection and tracking, an improved UAV detection algorithm based on YOLOv7 and DeepSort framework is proposed. …”
Get full text
Article -
24
LLD-YOLO: A Low-Light Object Detection Algorithm Based on Dynamic Weighted Fusion of Shallow and Deep Features
Published 2025-01-01“…Object detection in low-light scenarios has a wide range of applications, but existing algorithms often struggle to preserve the scarce low-level features in dark environments and exhibit limitations in localization accuracy for blurred edges and occluded objects, leading to suboptimal performance. …”
Get full text
Article -
25
Design and Implementation of Low-Complexity Multiple Symbol Detection Algorithm Using Hybrid Stochastic Computing in Aircraft Wireless Communications
Published 2025-03-01“…The Multiple Symbol Detection (MSD) algorithm can effectively lower the demodulation threshold in Frequency Modulation (FM) technology, which is widely used in aircraft wireless communications due to its insensitivity to large Doppler shifts. …”
Get full text
Article -
26
Detection of low-altitude infrared small targets for UAVs using a density-based artificial bee colony algorithm
Published 2025-07-01“…Abstract The objective of this paper is to address the issue of the inadequate detection accuracy of UAVs operating at low-altitudes in conditions of weak thermal signals. …”
Get full text
Article -
27
A Low Complexity Near-Optimal Detector Based on Teaching-Learning Algorithm for Massive MIMO
Published 2024-03-01“…In this paper, a low-complexity receiver is proposed using a Teaching-Learning based optimization (TLBO) heuristic algorithm for a large-scale system. …”
Get full text
Article -
28
An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting: a reader study
Published 2024-11-01“…Abstract Background To retrospectively assess the added value of an artificial intelligence (AI) algorithm for detecting pulmonary nodules on ultra-low-dose computed tomography (ULDCT) performed at the emergency department (ED). …”
Get full text
Article -
29
Application of CycleGAN-based low-light image enhancement algorithm in foreign object detection on belt conveyors in underground mines
Published 2025-07-01“…To address this issue, an improved CycleGAN-based low-illumination image enhancement algorithm is proposed, which employs a cycle generative adversarial network for unsupervised learning. …”
Get full text
Article -
30
-
31
A belief propagation algorithm based on track‐before‐detect for tracking low‐observable and manoeuvering targets using multiple sensors
Published 2024-12-01“…Abstract It is notoriously challenging work to track an unknown number of low‐observable manoeuvering targets. In this paper, a sequential Bayesian inference method based on the multiple‐model dynamic model and track‐before‐detect measurement (TBD) model is proposed for tracking low‐observable manoeuvering targets using multiple sensors. …”
Get full text
Article -
32
A low complexity pilot assignment algorithm based on user polar coordinates in CF-mMIMO systems
Published 2023-07-01“…Absrtact: In order to reduce the pilot contamination in the cell-free massive multi-input multi-output (MIMO) system, a low complexity pilot assignment algorithm based on user polar coordinates was proposed.Firstly, a Gaussian weighted density algorithm was proposed to determine a centroid as the polar coordinates center point in the system coverage area, then pre-assigned the pilot in order according to the angular coordinates, so that users who reused the same pilot had a greater probability of having a longer distance, and henced reduce the pilot contamination.A low complexity distance detection algorithm was then proposed to ensure that the user spacing between any two users multiplexing the same pilot was greater than the threshold.The simulation results show that the proposed pilot assignment algorithm effectively reduce pilot contamination, improve the uplink throughput of 95% users of the system, and achieve a good compromise between performance and complexity.…”
Get full text
Article -
33
Image forgery detection algorithm based on U-shaped detection network
Published 2019-04-01“…Aiming at the defects of traditional image tampering detection algorithm relying on single image attribute,low applicability and current high time-complexity detection algorithm based on deep learning,an U-shaped detection network image forgery detection algorithm was proposed.Firstly,the multi-stage feature information in the image by using the continuous convolution layers and the max-pooling layers was extracted by U-shaped detection network,and then the obtained feature information to the resolution of the input image through the upsampling operation was restored.At the same time,in order to ensure higher detection accuracy while extracting high-level semantic information of the image,the output features of each stage in U-shaped detection network would be merged with the corresponding output features through the upsampling layer.Further the hidden feature information between tampered and un-tampered regions in the image upon the characteristics of the general network was explored by U-shaped detection network,which could be realized quickly by using its end-to-end network structure and extracting the attributes of strong correlation information among image contexts that could ensure high-precision detection results.Finally,the conditional random field was used to optimize the output of the U-shaped detection network to obtain a more exact detection results.The experimental results show that the proposed algorithm outperforms those traditional forgery detection algorithms based on single image attribute and the current deep learning-based detection algorithm,and has good robustness.…”
Get full text
Article -
34
Image forgery detection algorithm based on U-shaped detection network
Published 2019-04-01“…Aiming at the defects of traditional image tampering detection algorithm relying on single image attribute,low applicability and current high time-complexity detection algorithm based on deep learning,an U-shaped detection network image forgery detection algorithm was proposed.Firstly,the multi-stage feature information in the image by using the continuous convolution layers and the max-pooling layers was extracted by U-shaped detection network,and then the obtained feature information to the resolution of the input image through the upsampling operation was restored.At the same time,in order to ensure higher detection accuracy while extracting high-level semantic information of the image,the output features of each stage in U-shaped detection network would be merged with the corresponding output features through the upsampling layer.Further the hidden feature information between tampered and un-tampered regions in the image upon the characteristics of the general network was explored by U-shaped detection network,which could be realized quickly by using its end-to-end network structure and extracting the attributes of strong correlation information among image contexts that could ensure high-precision detection results.Finally,the conditional random field was used to optimize the output of the U-shaped detection network to obtain a more exact detection results.The experimental results show that the proposed algorithm outperforms those traditional forgery detection algorithms based on single image attribute and the current deep learning-based detection algorithm,and has good robustness.…”
Get full text
Article -
35
Shot boundary detection algorithm based on ORB
Published 2013-11-01“…The existing algorithms of SBD show low robustness when there exist camera or object movements, light changes in the scene. …”
Get full text
Article -
36
-
37
Low-Complexity Gaussian Detection for MIMO Systems
Published 2010-01-01“…Using factor graphs as a general framework and applying the Gaussian approximation, three low-complexity iterative detection algorithms are derived, and their performances are compared by means of Monte Carlo simulations. …”
Get full text
Article -
38
Evaluation of Automated Object-Detection Algorithms for Koala Detection in Infrared Aerial Imagery
Published 2024-10-01“…The implementation of infrared cameras and drones has demonstrated encouraging outcomes, regardless of whether the detection was performed by human observers or automated algorithms. …”
Get full text
Article -
39
-
40
An efficient interpretable framework for unsupervised low, very low and extreme birth weight detection.
Published 2025-01-01“…This study presents an efficient and interpretable framework for unsupervised detection of low, very low, and extreme birth weights. …”
Get full text
Article
