Phishing detection algorithm based on attention and feature fusion by ZHANG Sirui, YAN Zhiwei, DONG Kejun, YUCHI Xuebiao

“…To address the problem of escalating adversarial phishing technologies, a phishing detection algorithm based on the attention mechanism and feature fusion was proposed, and a hierarchical classification model was established. …”
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Feature-based enhanced boosting algorithm for depression detection by Muhammad Sadiq Rohei, Kasturi Dewi Varathan, Shivakumara Palaiahnakote, Nor Badrul Anuar

Subjects: “…Feature-based enhanced boosting algorithm…”
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SIFT Feature-Based Video Camera Boundary Detection Algorithm by Lingqiang Kong

“…Aiming at the problem of low accuracy of edge detection of the film and television lens, a new SIFT feature-based camera detection algorithm was proposed. …”
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Lightweight Detection Algorithm for Breast-Mass Features in Ultrasound Images by Taojuan Li, Wen Liu, Mingxian Song, Zheng Gu, Ling Hai

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Object Detection Algorithm Based on Feature Enhancement and Anchor-object Matching by LI Cheng-yan, ZHAO Shuai, CHE Zi-xuan

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Research on Traffic Marking Segmentation Detection Algorithm Based on Feature Fusion by Zhonghe He, Zizheng Gan, Pengfei Gong, Min Li, Kailong Li

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A feature enhancement FCOS algorithm for dynamic traffic object detection by Tuqiang Zhou, Wei Liu, Haoran Li

“…Finally, in the detection part, the feature expression ability of shallow network was further enhanced by multi-scale feature fusion module, and the effectiveness of the proposed algorithm was verified using Cityscapes dataset. …”
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Spoofing speech detection algorithm based on joint feature and random forest by Jiaqi YU, Zhihua JIAN, Jia XU, Lin YOU, Yunlu WANG, Chao WU

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Spoofing speech detection algorithm based on joint feature and random forest by Jiaqi YU, Zhihua JIAN, Jia XU, Lin YOU, Yunlu WANG, Chao WU

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Video inter-frame tampering detection algorithm fusing multiple features by Hui XIAO, Bin WENG, Tianqiang HUANG, Han PU, Zehui HUANG

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Shot boundary detection algorithm based on ORB by Jiang-qi TANG, Lin-jiang XIE, Qing-sheng YUAN, Dong-ming ZHANG, Xiu-guo BAO, Wei Guo

“…In the detection process, the matching number and matching rate of the feature points were used to describe the characteristics of shot boundary, and the similarity degree curve was used to represent the relationship between image frames. …”
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Robust Adversarial Example Detection Algorithm Based on High-Level Feature Differences by Hua Mu, Chenggang Li, Anjie Peng, Yangyang Wang, Zhenyu Liang

“…To address these challenges, this paper introduces a novel adversarial example detection algorithm based on high-level feature differences (HFDs), which is specifically designed to improve robustness against both attacks and preprocessing operations. …”
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Fan Blade Crack Detection Algorithm Based on Multi-Scale Feature Fusion by Yongjun Qi, Hailin Tang, Altangerel Khuder

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Retracted: Detection Algorithm of Tennis Serve Mistakes Based on Feature Point Trajectory by Advances in Meteorology

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Novel Matching Algorithm for Effective Drone Detection and Identification by Radio Feature Extraction by Teng Wu, Yan Du, Runze Mao, Hui Xie, Shengjun Wei, Changzhen Hu

“…To address this challenge, this paper proposes a novel drone detection and identification algorithm based on transmission signal analysis. …”
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A Feature-Enhanced Small Object Detection Algorithm Based on Attention Mechanism by Zhe Quan, Jun Sun

“…However, due to factors such as the environment, lighting, altitude, and angle, UAV images face challenges like small object sizes, high object density, and significant background interference, making object detection tasks difficult. To address these issues, we use YOLOv8s as the basic framework and introduce a multi-level feature fusion algorithm. …”
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An improved air conditioner label defect detection algorithm based on SURF features by Huizi ZHOU, Yuelin LIU, Qing LIU, Jianwu LI

“…Aiming at the bottleneck that deep learning algorithms are not compatible with device detection and new sample collection,as well as poor detection timeliness and generalization ability,a traditional template matching detection algorithm based on SURF features was proposed. …”
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A range spread target detection algorithm based on polarimetric features and SVDD by Qiang LI, Yuanxin YAO, Xiangqi KONG

“…Multi-polarization range high resolution radar is an important mean for ground target detection.In the echo formed by it, the target occupies multiple range cells and becomes an extended target.The traditional spread target detection method relies on energy, and the detection performance decreases when the signal-to-clutter ratio decreases.A spread target detection algorithm based on polarization decomposition features was proposed, which improved the detection performance under low signal-to-clutter ratio by using the difference of polarization scattering characteristics between target and clutter.Specifically, 16 kinds of polarization decomposition features were extracted to form feature vectors as detection statistics, and then support vector data description (SVDD) was used to obtain the detection threshold.When training the detection threshold, the polarization decomposition features of clutter were extracted as training data.In order to ensure the false alarm probability, two penalty parameters were introduced into the objective function of SVDD.The experimental results show that the proposed method requires a signal-to-clutter ratio of about 12.6 dB in the case of Gobi background, false alarm probability of 10^-4 and detection probability of 90%, which is about 1.7 dB lower than the energy-based methods.…”
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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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Tree-Based Algorithms and Incremental Feature Optimization for Fault Detection and Diagnosis in Photovoltaic Systems by Khaled Chahine

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