MFA-net: Object detection for complex X-ray cargo and baggage security imagery. by Thanaporn Viriyasaranon, Seung-Hoon Chae, Jang-Hwan Choi

“…Second, the fusion feature pyramid network combines the proposed attention and fusion modules to enhance multiscale object recognition and alleviate the object and occlusion problem. …”
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An efficient bearing fault detection strategy based on a hybrid machine learning technique by Khalid Alqunun, Mohammed Bachir Bechiri, Mohamed Naoui, Abderrahmane Khechekhouche, Ismail Marouani, Tawfik Guesmi, Badr M. Alshammari, Amer AlGhadhban, Abderrahim Allal

“…Abstract This study introduces an innovative method for addressing the bearing fault detection problem in rotating machinery. The proposed approach integrates multi-feature extraction, advanced feature selection, and state-of-the-art classification techniques using convolutional neural network (CNN) models. …”
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Pest detection in dynamic environments: an adaptive continual test-time domain adaptation strategy by Rui Fu, Shiyu Wang, Mingqiu Dong, Hao Sun, Mohammed Abdulhakim Al-Absi, Kaijie Zhang, Qian Chen, Liqun Xiao, Xuewei Wang, Ye Li

“…The MT-DAM integrates an object detection model with an image segmentation model, exchanging information through feature fusion at the feature extraction layer. …”
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Safety belt wearing detection for electric aloft work based on EPSA-YOLOv5 by LI Yongfu, CHEN Libin, HUI Junwei, YUAN Runcong, CHAI Haokai

“…To address the problem of missed detection and slow detection speed in safety belt wearing test for electric aloft work, this paper proposed a method for detecting the wearing of safety belts based on EPSA-YOLOv5 algorithm. …”
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Lightning Damage Detection Method Using Autoencoder: A Case Study on Wind Turbines with Different Blade Damage Patterns by Takuto Matsui, Kazuki Matsuoka, Kazuo Yamamoto

“…There have been numerous reported accidents of lightning strikes damaging wind turbine blades, which poses a serious problem. In certain accidents, the blades that were struck by lightning continued to rotate, resulting in breakage due to centrifugal force. …”
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Surrounding Object Material Detection and Identification Method for Robots Based on Ultrasonic Echo Signals by Bo Zhu, Tao Geng, Gedong Jiang, Zheng Guan, Yang Li, Xialun Yun

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AReal-time Detection Method of Vehicle Target Based on Improved YOLOv5s Algorithm by CHEN Xiufeng, WANG Chengxin, WU Yuechen, GU Kexin

“…To improve the detection rate of small target vehicles，an optimization of the YOLOv5s algorithm network structure was established，which added a small target detection layer and spliced the shallow feature map with the deep feature map in the detection. …”
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The Development of a Lightweight DE-YOLO Model for Detecting Impurities and Broken Rice Grains by Zhenwei Liang, Xingyue Xu, Deyong Yang, Yanbin Liu

“…A rice impurity detection algorithm model, DE-YOLO, based on YOLOX-s improvement is proposed to address the issues of small crop target recognition and the similarity of impurities in rice impurity detection. …”
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Research on underwater disease target detection method of inland waterway based on deep learning by Tao Yu, Yu Xie, Jinsong Luo, Wei Zhu, Jie Liu

“…Abstract Aiming at the problems of low detection accuracy and poor generalization ability of underwater disease targets in inland waterways, an underwater disease target detection algorithm for inland waterways based on improved YOLOv5 is designed, which is denoted as YOLOv5-GBCE. …”
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A Malware Detection Method Based on Genetic Algorithm Optimized CNN-SENet Network by Zheng Yang, Hua Zhu, Zhao Li, Gang Wang, Meng Su

“…With the popularization of smart terminals and the gradual increase of power grid informatization and digitization, the protection of power monitoring systems from various cybersecurity threads is a current scientific problem that needs to be solved urgently. To this end, this paper proposes a malware detection method based on genetic algorithm optimization of the CNN-SENet network, which firstly introduces the SENet attention mechanism into the convolutional neural network to enhance the spatial feature extraction capability of the model; then, the application programming interface (API) sequences corresponding to different software behaviors are processed by segmentation and de-duplication, which in turn leads to the sequence feature extraction through the CNN-SENet model; finally, genetic algorithm is used to optimize the hyperparameters of CNN-SENet network to reduce the computational overhead of CNN and to achieve the recognition and classification of different malware at the output layer. …”
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An improve fraud detection framework via dynamic representations and adaptive frequency response filter by Juncheng Yang, Shuxia Li, Zijun Huang, Junhang Wu

“…Additionally, in LSN learning, we designed a trainable filter to capture differences between feature channels during information aggregation, mitigating the over-smoothing problem. …”
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Weighted Feature Fusion Network Based on Large Kernel Convolution and Transformer for Multi-Modal Remote Sensing Image Segmentation by Jianxia Wang, Shaozu Qiu, Jia Cai, Xiaoming Zhang

“…Finally, a Difference information Feature Fusion Module (DFFM) leveraging attention to differential regions is used to achieve cross-level feature fusion and enhance small object detection. …”
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MEIS-YOLO: Improving YOLOv11 for efficient aerial object detection with lightweight design by Yicheng Liu, Jinsong Wu, Li Chen

“…The core innovation of the model lies in the introduction of a Multi-scale Edge Information Selection (MEIS) module, which selects key features highly relevant to the target detection task from multi-scale features, strengthening the representation of edge information and significantly improving detection performance under conditions of small targets and complex backgrounds. …”
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MPAR-RCNN: a multi-task network for multiple person detection with attribute recognition by S. Raghavendra, S. K. Abhilash, Venu Madhav Nookala, Jayashree Shetty, Praveen Gurunath Bharathi

“…Multi-label attribute recognition is a critical task in computer vision, with applications ranging across diverse fields. This problem often involves detecting objects with multiple attributes, necessitating sophisticated models capable of both high-level differentiation and fine-grained feature extraction. …”
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A novel temporal classification prototype network for few-shot bearing fault detection by Yanfei Liu, Ziang Du, Hao Zheng, Qian Zhang, Cheng Chen, Nana Wu

“…At present, the problem of less fault data samples in the field of fault detection has caused great trouble to the research of deep learning. …”
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Dual-Branch Multi-Dimensional Attention Mechanism for Joint Facial Expression Detection and Classification by Cheng Peng, Bohao Li, Kun Zou, Bowen Zhang, Genan Dai, Ah Chung Tsoi

“…This paper addresses the central issue arising from the (SDAC) of facial expressions, namely, to balance the competing demands of good global features for detection, and fine features for good facial expression classifications by replacing the feature extraction part of the “neck” network in the feature pyramid network in the You Only Look Once X (YOLOX) framework with a novel architecture involving three attention mechanisms—batch, channel, and neighborhood—which respectively explores the three input dimensions—batch, channel, and spatial. …”
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Electricity Theft Detection Using Rule-Based Machine Leaning (rML) Approach by Sheyda Bahrami, Erol Yumuk, Alper Kerem, Beytullah Topçu, Ahmetcan Kaya

“…Power companies can use the information gathered by Advanced Metering Infrastructure (AMI) to create data-driven, machine learning-based approaches for Electricity Theft Detection (ETD) in order to solve this problem. The majority of data-driven methods for detecting power theft do take usage trends into account while doing their analyses. …”
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Multi-Scale Plastic Lunch Box Surface Defect Detection Based on Dynamic Convolution by Jing Yang, Gang Zhang, Yunwang Ge, Jingzhuo Shi, Yiming Wang, Jiahao Li

“…Firstly, this paper integrates the attention mechanism into Slim-neck, and enhances the model’s ability to perceive multi-scale feature information. Secondly, a small target detection layer is added to Slim-neck to solve the semantic information loss problem of various defect features. …”
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Improved M-ORB based direct-loop closure detection algorithm for visual SLAM by Wei LI, Menghan REN, Weihao HUANG, Xiaoyu DU, Yi ZHOU

“…Most kinds of direct methods do not extract image feature points in the front end of SLAM system, resulting in that they cannot use loop closure detection with bag-of-words models to eliminate the cumulative error of the system.To resolve this problem, an improved mature-oriented fast and rotated BRIEF (M-ORB) based direct-loop closure detection algorithm for visual SLAM was proposed, which designed an improved M-ORB, generated the bag of words model required for loop closure detection, and then used the term frequency-inverse document frequency (TF-IDF) algorithm to adaptively assign weights to the visual words in each sub-node of the dictionary tree.Finally, an accurate representation of the scene information was obtained.In the end, the proposed algorithm and conducted comparative experiments were verified though two public data sets TUM and KITTI.The experimental results show that the algorithm proposed in this paper can effectively detect the loop closure, and has better real-time and robustness performance without reducing the accuracy.…”
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Research on Face Local Attribute Detection Method Based on Improved SSD Network Structure by Qun Luo, Zhendong Liu

“…The existing face detection methods usually had the problem of low accuracy of face recognition in the environment of occlusion interference, which was limited when applied to the face detection task in complex scenes. …”
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