End-to-end scene text detection and recognition algorithm based on Transformer decoders by Jinzhi ZHENG, Ruyi JI, Libo ZHANG, Chen ZHAO

“…Aiming at the detection and recognition task of arbitrary shape text in scene, a novelty scene text detection and recognition algorithm which could be trained by end-to-end algorithm was proposed.Firstly, the detection branch of text aware module based on segmentation idea was introduced to detect scene text from visual features extracted by convolutional network.Then, a recognition branch based on Transformer vision module and Transformer language module encoded the text features of the detection results.Finally, the text features encoded by the fusion gate in the recognition branch were fused to output the scene text.The experimental results on the three benchmark datasets of Total-Text, ICDAR2013 and ICDAR2015 show that the proposed algorithm has excellent performance in recall, precision, F-score, and has certain advantages in efficiency.…”
Get full text

End-to-end scene text detection and recognition algorithm based on Transformer decoders by Jinzhi ZHENG, Ruyi JI, Libo ZHANG, Chen ZHAO

“…Aiming at the detection and recognition task of arbitrary shape text in scene, a novelty scene text detection and recognition algorithm which could be trained by end-to-end algorithm was proposed.Firstly, the detection branch of text aware module based on segmentation idea was introduced to detect scene text from visual features extracted by convolutional network.Then, a recognition branch based on Transformer vision module and Transformer language module encoded the text features of the detection results.Finally, the text features encoded by the fusion gate in the recognition branch were fused to output the scene text.The experimental results on the three benchmark datasets of Total-Text, ICDAR2013 and ICDAR2015 show that the proposed algorithm has excellent performance in recall, precision, F-score, and has certain advantages in efficiency.…”
Get full text

Detecting subsurface diseases on airport road surface based on an improved SSD algorithm. by Mengmeng Pan, Huiguang Chen, Lipeng Yang, XianRong Jiang

“…Facing the radar data of underground hidden diseases of airport roads with low recognition and high noise intensity, it is inefficient to recognize the diseases by manual identification, and it is difficult to achieve accurate differentiation and localization of the diseases by the existing detection methods. We analyze and propose an improved algorithm EFA-SSD (Enhanced Feature Aggregation SSD) for automatic detection of airport road subsurface diseases, which solves the problems of strong noise background conditions, severe interference of morphological features of different types of subsurface diseases, and low target recognition. …”
Get full text

Low scaling factor Seam Carving tamper detection algorithm with hybrid attention by ZHAO Jie, CHANG Haochan, WU Bin

“…The existing seam carving tamper detection algorithms have the problems of low detection accuracy and weak robustness for the case of low scaling factor. …”
Get full text

Infrared small target detection algorithm with U-shaped multiscale transformer network by DUAN Peipei, ZHANG Yan, LUO Mingshi, YAN Xiaoying

Get full text

Deep Learning-Based Intelligent Detection Algorithm for Surface Disease in Concrete Buildings by Jing Gu, Yijuan Pan, Jingjing Zhang

Get full text

Deep learning-based edge detection for random natural images by Kanija Muntarina, Rafid Mostafiz, Sumaita Binte Shorif, Mohammad Shorif Uddin

“…In recent years, the emergence of deep learning technology has revolutionized this field by utilizing its ability to automatically learn complex features from natural images. Deep learning approaches offer significant advantages in capturing high-level representations, thereby improving the accuracy and robustness of edge detection algorithms. …”
Get full text

Small Ship Detection Based on Improved Neural Network Algorithm and SAR Images by Jiaqi Li, Hongyuan Huo, Li Guo, De Zhang, Wei Feng, Yi Lian, Long He

“…Therefore, based on the YOLOv5s model, this paper improves its backbone network and feature fusion network algorithm to improve the accuracy of ship detection target recognition. …”
Get full text

HRDA-Net: image multiple manipulation detection and location algorithm in real scene by Ye ZHU, Yilin YU, Yingchun GUO

Get full text

Detecting Pronunciation Errors in Spoken English Tests Based on Multifeature Fusion Algorithm by Yinping Wang

Get full text

Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm by Kuan-Cheng Lin, Sih-Yang Chen, Jason C. Hung

Get full text

Enhanced APT detection with the improved KAN algorithm: capturing interdependencies for better accuracy by Weiwu Ren, Hewen Zhang, Yu Hong, Zhiwei Wang

Get full text

Intrusion detection algorithm of wireless network based on network traffic anomaly analysis by Xiangqian Nie, Jiao Xing, Qimeng Li, Fan Xiao

“…These features are analyzed by an intrusion detection module combining particle swarm optimization and support vector machine algorithms. …”
Get full text

3L-YOLO: A Lightweight Low-Light Object Detection Algorithm by Zhenqi Han, Zhen Yue, Lizhuang Liu

“…In comparison to the LOL-YOLO low-light object detection algorithm, 3L-YOLO requires 16.9 GFLOPs, representing a reduction of 4 GFLOPs.…”
Get full text

Research on Fabric Defect Detection Algorithm Based on Lightweight YOLOv7-Tiny by Tang Li, Mei Shunqi, Shi Yishan, Zhou Shi, Zheng Quan, Hongkai Jiang, Xu Qiao, Zhang Zhiming

“…The current advanced neural network models are expanding in size and complexity to achieve improved detection accuracy. This study designs a lightweight fabric defect detection algorithm based on YOLOv7-tiny, called YOLOv7-tiny-MGCK. …”
Get full text

YOLO-DKM: A Flame and Spark Detection Algorithm Based on Deep Learning by Linpo Shang, Xufei Hu, Zijian Huang, Qiang Zhang, Zhiyu Zhang, Xin Li, Yanzuo Chang

“…In the field of computer vision, existing fire detection algorithms often have problems such as low detection accuracy and algorithm error detection. …”
Get full text

YOLOv8-E: An Improved YOLOv8 Algorithm for Eggplant Disease Detection by Yuxi Huang, Hong Zhao, Jie Wang

“…Therefore, timely and effective detection of eggplant diseases is crucial. Deep learning-based object detection algorithms can automatically extract features from images of eggplants affected by diseases. …”
Get full text

Res-DNN based signal detection algorithm for end-to-end MIMO systems by Guoquan LI, Yonghai XU, Jinzhao LIN, Zhengwen HUANG

“…Deep learning can improve the effect of signal detection by extracting the inherent characteristics of wireless communication data.To solve the tradeoff between the performance and complexity of MIMO system signal detection, an end-to-end MIMO system signal detection scheme based on deep learning was proposed.The encoder and the decoder based on residual deep neural network replace the transmitter and the receiver of the wireless communication system respectively, and they were trained in an end-to-end manner as a whole.Firstly, the features of the input data were extracted by encoder, then the communication model was established and was sent to the zero forcing detector for preliminary detection.Finally, the detection signal was reconstructed through the decoder.Simulation results show that the proposed detection scheme is superior to the same type of algorithm, and the detection performance is significantly better than that of the MMSE detection algorithm at the expense of a certain time complexity.…”
Get full text

Res-DNN based signal detection algorithm for end-to-end MIMO systems by Guoquan LI, Yonghai XU, Jinzhao LIN, Zhengwen HUANG

“…Deep learning can improve the effect of signal detection by extracting the inherent characteristics of wireless communication data.To solve the tradeoff between the performance and complexity of MIMO system signal detection, an end-to-end MIMO system signal detection scheme based on deep learning was proposed.The encoder and the decoder based on residual deep neural network replace the transmitter and the receiver of the wireless communication system respectively, and they were trained in an end-to-end manner as a whole.Firstly, the features of the input data were extracted by encoder, then the communication model was established and was sent to the zero forcing detector for preliminary detection.Finally, the detection signal was reconstructed through the decoder.Simulation results show that the proposed detection scheme is superior to the same type of algorithm, and the detection performance is significantly better than that of the MMSE detection algorithm at the expense of a certain time complexity.…”
Get full text

Anomaly detection algorithm based on Gaussian mixture variational auto encoder network by Huahua CHEN, Zhe CHEN, Chunsheng GUO, Na YING, Xueyi YE, Jianwu ZHANG

“…Anomalous data, which deviates from a large number of normal data, has a negative impact and contains a risk on various systems.Anomaly detection can detect anomalies in the data and provide important support for the normal operation of various systems, which has important practical significance.An anomaly detection algorithm based on Gaussian mixture variational auto encoder network was proposed, in which a variational autoencoder was built to extract the features of the input data based on Gaussian mixture distribution, and using this variational autoencoder to construct a deep support vector network to compress the feature space and find the minimum hyper sphere to separate the normal data and the abnormal data.Anomalies can be detected by the score from the Euclidean distance from the feature of data to the center of the hypersphere.The proposed algorithm was evaluated on the benchmark datasets MNIST and Fashion-MNIST, and the corresponding average AUC are 0.954 and 0.937 respectively.The experimental results show that the proposed algorithm achieves preferable effects.…”
Get full text