MAS-YOLOv11: An Improved Underwater Object Detection Algorithm Based on YOLOv11 by Yang Luo, Aiping Wu, Qingqing Fu

Get full text

Small target detection algorithm based on SAHI-Improved-YOLOv8 for UAV imagery: A case study of tree pit detection by Xiuhao Liang, Jun Xiang, Sheng Qin, Yundan Xiao, Lifen Chen, Dongxia Zou, Honglun Ma, Dong Huang, Yongxin Huang, Wei Wei

“…To address these challenges, this paper proposes a small target detection algorithm based on SAHI-ImprovedYOLOv8 for detecting tree pits, which includes Slicing Aided Hyper Inference (SAHI), Focal loss, Spatial Pyramid Pooling Concurrent Spatial Pyramid Convolution (SPPCSPC), and Convolutional Block Attention Module (CBAM). …”
Get full text

Role of Artificial Intelligence in Detecting Pneumothorax and Cardiomegaly in Chest X-rays: An Observational Study by Manasa Mayukha Hanumanthu, Harsha Kopuru, Bala Murali Krishna Vadana, Sandeep Velicheti, Sai Preethi Athota, Anveeksha Marineni, Chandra Sekhar Kondragunta

Subjects: Get full text

Computer-Aid System for Automated Jaundice Detection by Ahmad Yaseen Abdulrazzak, Saleem Latif Mohammed, Ali Al-Naji, Javaan Chahl

“…In this study, jaundice or hyperbilirubinemia is diagnosed using a computer vision system based on a random forest algorithm. The system comprises a digital HD camera, a computer device with a Matlab application installed to analyze and detect the skin color changes of the infant, and an Arduino Uno microcontroller to control an LED ultraviolet light. …”
Get full text

Hybrid Technique Used for Straight Line Detection by Fawziya Ramo, Nidhal Al-Assady, Khalil Al-Saif

Subjects: Get full text

Energy efficient cooperative spectrum sensing algorithm in cognitive wireless sensor networks by Gui-cai YU, Cheng-zhi LONG, Man-tian XIANG

“…The analy-sis and simulation results show that the proposed algorithm can effectively reduce total energy consumption in cognitive sensor networks, improve energy efficiency.…”
Get full text

Automatic Evaluation Algorithms for Radio Tomography Imaging Methods by Krzysztof Strzecha, Grzegorz Rybak

Get full text

A novel approach to intrusion detection system using hybrid flower pollination and cheetah optimization algorithm by Deepshikha Kumari, Prashant Pranav, Abhinav Sinha, Sandip Dutta

“…A novel hybrid IDS model integrating the Flower Pollination Algorithm (FPA), Cheetah Optimization Algorithm (COA), and Artificial Neural Networks (ANN) is proposed to enhance detection accuracy, reduce false positives, and optimize feature selection, anomaly detection, and rule adaptation. …”
Get full text

Enhanced intrusion detection model based on principal component analysis and variable ensemble machine learning algorithm by Ayuba John, Ismail Fauzi Bin Isnin, Syed Hamid Hussain Madni, Farkhana Binti Muchtar

Subjects: Get full text

GIRH-Unet: Improved Residual Tobacco Segmentation Algorithm Based on GhostNetV3-Unet by Jianhua Ye, Yunda Zhang, Pan Li, Ze Guo

“…These factors contribute to the reduced accuracy and robustness of visual detection technologies based on segmentation algorithms within tobacco intelligent production systems, highlighting the need for a targeted segmentation model. …”
Get full text

Automated detection of spreading depolarizations in electrocorticography by Sreekar Puchala, Ethan Muchnik, Anca Ralescu, Jed A. Hartings

Subjects: Get full text

HUMAN EMOTION RECOGNITION SYSTEM USING DEEP LEARNING ALGORITHMS by Kateryna Yuvchenko, Valentyn Yesilevskyi, Olena Sereda

Get full text

Cervical Cancer Detection Using Deep Neural Network and Hybrid Waterwheel Plant Optimization Algorithm by Sarah A. Alzakari, Amel Ali Alhussan, S.K. Towfek, Marwa Metwally, Dina Ahmed Salem

Get full text

Insulator discharge severity assessment algorithm based on RDIDSNet by Cheng Chi, Li Keyu, Yanhui Meng, Yang Yang, JiNing Zhao, Shaotong Pei, Haosen Sun

“…The experimental results show that compared with the original YOLOv8, the RDIDSNet algorithm proposed in this paper achieves a detection speed of 61 Frames/s while realizing a detection accuracy of 78.1%, which can satisfy the demand for fast and accurate assessment of insulator discharge severity on edge devices.…”
Get full text

A Hybrid Algorithm for Contour Thinning in Image Processing by Mamatov Narzullo, Niyozmatova Nilufar, Jalelova Malika, Samijonov Abdurashid, Samijonov Boymirzo

Get full text

Adversarial patch defense algorithm based on PatchTracker by Zhenjie XIAO, Shiyu HUANG, Feng YE, Liqing HUANG, Tianqiang HUANG

“…The application of deep neural networks in target detection has been widely adopted in various fields.However, the introduction of adversarial patch attacks, which add local perturbations to images to mislead deep neural networks, poses a significant threat to target detection systems based on vision techniques.To tackle this issue, an adversarial patch defense algorithm based on PatchTracker was proposed, leveraging the semantic differences between adversarial patches and image backgrounds.This algorithm comprised an upstream patch detector and a downstream data enhancement module.The upstream patch detector employed a YOLOV5 (you only look once-v5) model with attention mechanism to determine the locations of adversarial patches, thereby improving the detection accuracy of small-scale adversarial patches.Subsequently, the detected regions were covered with appropriate pixel values to remove the adversarial patches.This module effectively reduced the impact of adversarial examples without relying on extensive training data.The downstream data enhancement module enhanced the robustness of the target detector by modifying the model training paradigm.Finally, the image with removed patches was input into the downstream YOLOV5 target detection model, which had been enhanced through data augmentation.Cross-validation was performed on the public TT100K traffic sign dataset.Experimental results demonstrated that the proposed algorithm effectively defended against various types of generic adversarial patch attacks when compared to situations without defense measures.The algorithm improves the mean average precision (mAP) by approximately 65% when detecting adversarial patch images, effectively reducing the false negative rate of small-scale adversarial patches.Moreover, compared to existing algorithms, this approach significantly enhances the accuracy of neural networks in detecting adversarial samples.Additionally, the method exhibited excellent compatibility as it does not require modification of the downstream model structure.…”
Get full text

Malware detection approach based on improved SOINN by Bin ZHANG, Lixun LI, Shuqin DONG

Subjects: “…SOINN algorithm…”
Get full text

Campus risk detection using the S-YOLOv10-SIC network and a self-calibrated illumination algorithm by Qiang Zhao, Sha Liu, Shihao Zhang, Baijuan Wang

Subjects: “…Campus risk detection…”
Get full text

VRU-YOLO: A Small Object Detection Algorithm for Vulnerable Road Users in Complex Scenes by Yunxiang Liu, Yuqing Shi

“…To overcome these challenges, this paper proposes an improved VRU detection algorithm based on YOLOv8, named VRU-YOLO. …”
Get full text

A low complexity detection algorithm for large scale multiuser MIMO based on message passing by Qiong WANG, Wei YE, Mingming JI

“…According to the problem of high complexity of base station detection in large scale multiuser multiple input multiple output (MIMO) system,a low complexity multiuser variable node full information Gaussian message passing iterative detection algorithm based on forced convergence (VFI-GMPID-FC) was proposed.Firstly,the traditional Gaussian message passing iterative detection (GMPID) algorithm was improved to obtain VFI-GMPID algorithm,the detection performance of the VFI-GMPID algorithm approximates the minimum mean square error detection (MMSE) algorithm,but the complexity was considerably less than the MMSE algorithm.Then,the VFI-GMPID-FC algorithm was proposed to reduce the complexity of the algorithm and improve the detection efficiency.Finally,the simulation results show that the proposed algorithm can effectively reduce the algorithm complexity while ensuring the detection performance.…”
Get full text