Robust Adversarial Example Detection Algorithm Based on High-Level Feature Differences by Hua Mu, Chenggang Li, Anjie Peng, Yangyang Wang, Zhenyu Liang

“…In response, various defense strategies have been proposed, including adversarial example detection. A range of detection algorithms has been developed to differentiate between benign samples and adversarial examples. …”
Get full text

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.…”
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

YOLO-MMCE algorithm for visual detection of engineering vehicles in high-consequence area by Huimei SUN, Lu LIU, Degang WANG, Taiyong WANG

“…This paper addresses the shortcomings of current common techniques for detecting overlapped targets of engineering vehicles and target detection in scenarios with varying sunlight, highlighting issues such as high miss rates and low detection accuracies. …”
Get full text

Lightweight Apple Leaf Disease Detection Algorithm Based on Improved YOLOv8 by LUO Youlu, PAN Yonghao, XIA Shunxing, TAO Youzhi

“…To reduce the incidence of apple diseases and increase fruit yield, developing efficient and fast apple leaf disease detection technology is of great significance. An improved YOLOv8 algorithm was proposed to identify the leaf diseases that occurred during the growth of apples.…”
Get full text

Machine learning for Internet of things anomaly detection under low-quality data by Shangbin Han, Qianhong Wu, Yang Yang

“…However, the existing schemes ignore the problem of data quality, which leads to the unsatisfactory detection effect in practice. Therefore, practitioners may not know which algorithm to choose due to the lack of review and evaluation of anomaly detection methods under low-quality data. …”
Get full text

Detection of Thermal Emission at Millimeter Wavelengths from Low-Earth Orbit Satellites by Allen Foster, A. Chokshi, A. J. Anderson, B. Ansarinejad, M. Archipley, L. Balkenhol, K. Benabed, A. N. Bender, D. R. Barron, B. A. Benson, F. Bianchini, L. E. Bleem, F. R. Bouchet, L. Bryant, E. Camphuis, J. E. Carlstrom, C. L. Chang, P. Chaubal, P. M. Chichura, T. -L. Chou, A. Coerver, T. M. Crawford, C. Daley, T. de Haan, K. R. Dibert, M. A. Dobbs, A. Doussot, D. Dutcher, W. Everett, C. Feng, K. R. Ferguson, K. Fichman, S. Galli, A. E. Gambrel, R. W. Gardner, F. Ge, N. Goeckner-Wald, R. Gualtieri, F. Guidi, S. Guns, N. W. Halverson, E. Hivon, G. P. Holder, W. L. Holzapfel, J. C. Hood, A. Hryciuk, N. Huang, F. Kéruzoré, A. R. Khalife, L. Knox, K. Kornoelje, M. Korman, C. -L. Kuo, K. Levy, A. E. Lowitz, C. Lu, A. Maniyar, E. S. Martsen, F. Menanteau, M. Millea, J. Montgomery, Y. Nakato, T. Natoli, G. I. Noble, Y. Omori, Z. Pan, P. Paschos, K. A. Phadke, A. W. Pollak, K. Prabhu, W. Quan, M. Rahimi, A. Rahlin, C. L. Reichardt, M. Rouble, J. E. Ruhl, E. Schiappucci, J. A. Sobrin, A. A. Stark, J. Stephen, C. Tandoi, B. Thorne, C. Trendafilova, C. Umilta, J. D. Vieira, A. Vitrier, Y. Wan, N. Whitehorn, W. L. K. Wu, M. R. Young, J. A. Zebrowski

Get full text

Low-Cost LiDAR Mapping on Bicycles for Urban Road and Sidewalk Detection by T. Bhatia, S. Goel, A. Medury

Get full text

Low Earth orbit satellite switching and recovery method based on generic congestion control algorithm by Hao Chen, Hang Yu, Hui Zhang, Huancheng Chen, Xiangsen Kong, Huaichuan Li, Demao Qing

“…To improve the user experience for video services and reduce communication interruptions and network congestion during link switching, this study proposes a LEO satellite switching recovery method based on an enhanced congestion control algorithm. The method includes a neural network-based attitude control strategy, a handover detection mechanism incorporating the satellite’s remaining service time, and a rate recovery algorithm combining congestion control with time series prediction. …”
Get full text

Adaptive Learning Algorithms for Low Dose Optimization in Coronary Arteries Angiography: A Comprehensive Review by Komal Tariq, Muhammad Adnan Munir, Hafiza Tooba Aftab, Amir Naveed, Ayesha Yousaf, Sajjad Ul Hassan

“…The article explores the integration of adaptive learning algorithms to optimize low-dose imaging in coronary artery angiography. …”
Get full text

Detecting LDoS attack based on ASPQ by Jing ZHANG, Hua-ping HU, Bo LIU, Feng-tao XIAO

Get full text

An advanced method for surface damage detection of concrete structures in low-light environments based on image enhancement and object detection networks by Tianyong Jiang, Lin Liu, Chunjun Hu, Lingyun Li, Jianhua Zheng

“…Abstract Surface damage detection in concrete structures is critical for maintaining structural integrity, yet current object detection algorithms often struggle in low-light environments. …”
Get full text

Evaluating the generalisability of region-naïve machine learning algorithms for the identification of epilepsy in low-resource settings. by Ioana Duta, Symon M Kariuki, Anthony K Ngugi, Angelina Kakooza Mwesige, Honorati Masanja, Daniel M Mwanga, Seth Owusu-Agyei, Ryan Wagner, J Helen Cross, Josemir W Sander, Charles R Newton, Arjune Sen, Gabriel Davis Jones

Get full text

STDE-YOLOv5s: a traffic sign detection algorithm based on context information enhancement by Qi Tian, Juwei Zhang

“…Considering the strong capability of the YOLOv5s algorithm in small object detection, its relatively low number of parameters, and ease of deployment, this paper selects YOLOv5s as the base model and proposes an improved traffic sign detection algorithm. …”
Get full text

Lightweight visible damage detection algorithm for embedded systems applied to pipeline automation equipment by Jiale Xiao, Lei Xu, Changyun Li, Ling Tang, Guogang Gao

“…The backbone of the algorithm, CSPHet, employs efficient combinatorial convolution and heterogeneous kernel convolution, incorporates a lightweight convolutional structure SL in the neck of the network, enhances the nonlinear representation and feature processing capability through channel shuffling, utilizes the lightweight self-attention mechanism Detect_SA for prediction, and employs a multilayered GhostConv to improve the computational efficiency. …”
Get full text

USD-YOLO: An Enhanced YOLO Algorithm for Small Object Detection in Unmanned Systems Perception by Hongqiang Deng, Shuzhe Zhang, Xiaodong Wang, Tianxin Han, Yun Ye

“…In the perception of unmanned systems, small object detection faces numerous challenges, including small size, low resolution, dense distribution, and occlusion, leading to suboptimal perception performance. …”
Get full text

A Multiuser Detection Algorithm in the Uplink SC-FDMA System for Green Communication Network by Peng Bao, Qingyang Guan, Mingxiang Guan

Get full text

Adaptive random early detection algorithm based on network traffic level grade prediction by Debin WEI, Chengsheng PAN, Li YANG, Zuoren YAN

“…In view of the problem that the calculation of average queue length and maximum packet drop probability in random early detection algorithm and its variants reflect the changes of network traffic slowly, an adaptive random early detection algorithm based on network traffic level grade prediction was proposed.Based on the statistical characteristics of self-similar network traffic, the transition probability table of network traffic level grade was established, and a grade prediction method of self-similar network traffic level with low complexity and high accuracy was proposed.Furthermore, the prediction results were applied to calculate the average queue length in equal interval and adjust the maximum packet drop probability.Under the condition of fixed and variable bottleneck link capacity, it is found that regardless of the degree of self-similarity of network traffic, the proposed algorithm can improve the throughput and packet loss rate, especially when the Hurst parameter is large and the traffic is light.…”
Get full text

A High-Accuracy Underwater Object Detection Algorithm for Synthetic Aperture Sonar Images by Jiahui Su, Deyin Xu, Lu Qiu, Zhiping Xu, Lixiong Lin, Jiachun Zheng

“…To improve these problems, this paper proposes a high-accuracy underwater object detection algorithm for SAS images, named the HAUOD algorithm. …”
Get full text

Pilot spoofing detection algorithm for edge nodes based on heterogeneous pilot energy estimation by Shiguo WANG, Shujuan TIAN, Qingyong DENG

“…For the federated learning scenarios with edge-end cooperation, edge servers and device terminals update their models and exchange gradient parameters frequently, and hence eavesdroppers can manipulate channel estimation through pilot spoofing to intercept the transmitted information and reduce the update efficiency of federated learning model.Therefore, a pilot attack detection algorithm with heterogeneous pilot energy estimation was proposed.Firstly, a federated learning pilot attack system model was constructed after the security of pilot attacks on data transmission had been analyzed.Then, a pilot attack detection method based on random segmentation and encryption methods was proposed to detect the pilot spoofing accurately and the contaminated channel could be recovered as well.Experimental results show that the proposed algorithm is more suitable for detecting pilot attacks with low transmit power and strong concealment compared to other existing algorithms.Furthermore, the data transmission rate of edge servers is improved significantly through the precoding based on the recovered channel state information.…”
Get full text