Image forgery detection algorithm based on U-shaped detection network by Zhuzhu WANG

“…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.…”
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An underground coal mine multi-target detection algorithm by FAN Shoujun, CHEN Xilin, WEI Liangyue, WANG Qingyu, ZHANG Shiyuan, DONG Fei, LEI Shaohua

“…To address these issues, based on the single-stage target detection algorithm YOLOv8n, this study proposed an underground coal mine multi-target detection algorithm based on feature extraction by dynamic snake convolution (FEDSC)-feature fusion by bi-directional feature pyramid network and semantic and detail fusion (FFBD). …”
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Tree-Based Algorithms and Incremental Feature Optimization for Fault Detection and Diagnosis in Photovoltaic Systems by Khaled Chahine

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Hybrid metaheuristic optimization for detecting and diagnosing noncommunicable diseases by Saleem Malik, S. Gopal Krishna Patro, Chandrakanta Mahanty, Saravanapriya Kumar, Ayodele Lasisi, Quadri Noorulhasan Naveed, Anjanabhargavi Kulkarni, Abdulrajak Buradi, Addisu Frinjo Emma, Naoufel Kraiem

“…These algorithms aim to address the challenges of feature selection, computational complexity, and disease classification accuracy. …”
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Nighttime Vehicle Detection Algorithm Based on Improved YOLOv7 by Fan Zhang

“…Aiming at the problems of low visibility, fuzzy target features and high leakage rate of small targets in nighttime vehicle detection, this paper proposes a nighttime vehicle detection algorithm E-YOLOv7 based on the improved YOLOv7. …”
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YOLO-v4 Small Object Detection Algorithm Fused With L-α by ZHANG Ning, YU Ming, REN Honge, AO Rui, ZHAO Long

“… The detection ability for small object is still need to be improved urgently in spite of the rapidly developing object detection technology based on deep learning at present.Compared with large objects, small object detection tasks hold drawbacks of low resolution and feature loss which leads to that many general algorithms cannot be directly applied to small object detection.The feature pyramid fusion can effectively combine the features of deep and shallow layers to enhance the performance.To solve the problem most models existing ignoring the imbalance of information during the feature fusion between adjacent layers, it is proposed to integrate the idea of fusion factor into the PANet of YOLOv4, use the fusion factor L-αto control the amount of information transmitted from the deep layer to the shallow, so as to effectively improve the efficiency of information fusion and enhance the ability of YOLO-v4 for small objects detection.With the addition of L-αin YOLO- V4 model, the experiment results show that the APtiny50and APsmall50on the TinyPerson are improved by 2.14% and 1.85% respectively, while the AP and APS on the MS COCO are separately increased by 1.4% and 2.7%.It is proved that this improved method is effective for small object detection with the evidence of better result than other small object detection algorithms.…”
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Effective Feature Selection on Transfer Deep Learning Algorithm for Thyroid Nodules Ultrasound Detection by Ghufran Basim Alghanimi, Hadeel Aljobouri, Khaleel Akeash Alshimmari, Rasha Massoud

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A Lightweight Pavement Defect Detection Algorithm Integrating Perception Enhancement and Feature Optimization by Xiang Zhang, Xiaopeng Wang, Zhuorang Yang

“…To address the current issue of large computations and the difficulty in balancing model complexity and detection accuracy in pavement defect detection models, a lightweight pavement defect detection algorithm, PGS-YOLO, is proposed based on YOLOv8, which integrates perception enhancement and feature optimization. …”
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Target Detection Algorithm Based on Global Feature Fusion in Parallel Dual Path Backbone by QIU Yunfei, XIN Hao

“…To solve these problems, a target detection algorithm based on global feature fusion in parallel dual path backbone is proposed. …”
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Urban object detection algorithm based on feature enhancement and progressive dynamic aggregation strategy by Luxuan Bian, Zijun Gao, Jue Wang, Bo Li

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RETRACTED ARTICLE: Multi-stage biomedical feature selection extraction algorithm for cancer detection by Ismail Keshta, Pallavi Sagar Deshpande, Mohammad Shabaz, Mukesh Soni, Mohit kumar Bhadla, Yasser Muhammed

“…This work proposes a Multi-stage algorithm for Biomedical Deep Feature Selection (MBDFS) to address this issue. …”
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Adaptive Robust Low-Beam Feature Point Detection Algorithm Based on Convex Hull by Qiqi Shen, Jie Wu

“…The existing algorithms for detecting low-beam feature points are neither universally applicable nor effective. …”
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Retraction Note: Multi-stage biomedical feature selection extraction algorithm for cancer detection by Ismail Keshta, Pallavi Sagar Deshpande, Mohammad Shabaz, Mukesh Soni, Mohit kumar Bhadla, Yasser Muhammed

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YOLO-SWD—An Improved Ship Recognition Algorithm for Feature Occlusion Scenarios by Ruyan Zhou, Mingkang Gu, Haiyan Pan

“…This study aims to enhance the accuracy and robustness of ship recognition by improving deep learning-based object detection models, enabling the algorithm to perform ship detection and recognition tasks effectively in feature-occluded scenarios. …”
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Optimizing Feature Selection and Machine Learning Algorithms for Early Detection of Prediabetes Risk: Comparative Study by Mahmoud B Almadhoun, MA Burhanuddin

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Research on the Object Detection Algorithm Based on Fisheye Image by GAO Qun, ZHU Jun, WANG Qianqian, CAO Jie, XU Chao

“…It uses the feature pyramid structure to detect multi-scale objects, and combines the rotation and distortion characteristics of the fisheye to optimize the algorithm and directly detects the objects from the original fisheye image. …”
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AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features by Ruochen Zhang, Hyeung-Sik Choi, Dongwook Jung, Phan Huy Nam Anh, Sang-Ki Jeong, Zihao Zhu

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MF-YOLO: Mask Wearing Detection Algorithm for Dense Environments by Peng Wen, Zhengyi Yuan, Junhu Zhang, Haitao Li

“…Additionally, a residual self-attention module is designed to capture fine edge position features by combining global feature extraction with sliding window-based information interaction, effectively reducing missed detections, especially for small objects. …”
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Small-Target Detection Algorithm Based on STDA-YOLOv8 by Cun Li, Shuhai Jiang, Xunan Cao

“…To address the issues of false positives and missed detections in small-target detection scenarios, a new algorithm based on STDA-YOLOv8 is proposed for small-target detection. …”
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Technologies and Algorithms for Building the Augmented Reality by I. A. Blagoveshchenskiy, N. A. Demyankov

“…In order to analyze video stream and recognize known objects in it, algorithms of the Computer Vision are used. The authors give a short description and the main characteristics only of two of them: genetic algorithms and feature detection & description. …”
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