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Infrared Small Target Detection with UAV Based on Convolutional Neural Networks

engineering Letters 2025年第5期33卷 1505-1512页

作者： Zhang, Jingshi Zhang, Yujun School of Computer and Software Engineering University of Science and Technology Liaoning Anshan 114051 China

Infrared target detection is now applied in many fields, such as medical imaging, military detection, autonomous driving, and environmental monitoring with drones. Due to the small size of these targets, complex environments, and limited contextual information, target detection is challenging. Therefore, this paper proposes an improved method for detecting small infrared targets based on the YOLOv9-c ***, we propose dynamic snake convolution (DSConv), a mechanism designed to precisely model tubular structures by adaptively concentrating on elongated and intricate curvilinear features within local regions. Second, we employ a new spatial and channel synergistic attention module (SCSA), integrating the SCSA module into the neck network of YOLOv9. The SCSA framework integrates two synergistic components, the Shared Multi-Semantic Spatial Attention (SMSA) module and the Progressive Channel Self-Attention (PCSA) module. This enhances the synergy between spatial and channel attention across multiple semantic levels. Finally, we introduce the C2F module into multi-scale dilated attention, altering its original dilation rates. C2F MSDA better aggregates semantic information, effectively reducing the redundancy of self-attention mechanisms. This improvement has shown promising results on the public HIT-UAV dataset, raising the average precision to 0.902 and providing potential technological support for the detection of small infrared targets. © 2025, International Association of Engineers. All rights reserved.

关键词： Attention mechanism Dynamic convolution Infrared small target detection YOLOv9

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Multi-Task Chinese Speech Recognition Method Based on the Squeezeformer Model

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2025年第1期52卷 23-31页

作者： Guo, Ying Wang, Li School of Computer Science and software Engineering University of Science and Technology Liaoning Anshan114051 China College of Computer Science and Technology Liaoning Anshan114051 China

End-to-end training has emerged as a prominent trend in speech recognition, with Conformer models effectively integrating Transformer and CNN architectures. However, their complexity and high computational cost pose deployment challenges. To address these issues, we propose a multi-task Chinese speech recognition method based on the Squeezeformer model. We replace the FMCF structure in Conformer with an MF/CF structure, leveraging the convolutional module as a local Multi- Head Attention (MHA) module to enhance efficiency. Multilevel down-sampling and up-sampling using a time-series U-Net further reduce computational costs. By eliminating redundant LayerNorm layers and employing depthwise separable convolutions, we streamline the model, reduce parameters, and lower deployment costs. An Adaptor Layer is integrated into the MHSA module to mitigate the vanishing gradient problem, and a ScaleVar Layer is added to enhance flexibility. Additionally, the RealFormer module is introduced on the decoding side to improve context understanding. Combining Connectionist Temporal Classification (CTC) with attention-based encoding and decoding models for multi-task learning improves performance and accuracy. Experimental results show that the proposed method reduces the parameters on AISHELL-1 dataset by 16% and reduces the character error rate to 5.50%. At the same time, it also shows good performance on AISHELL-2 dataset. © (2025), (International Association of Engineers). All rights reserved.

关键词： Speech recognition

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System for Alcohol Detection and Drowsiness Monitoring Based on Face and Drowsiness Recognition Model

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Journal of Institute of Control, Robotics and Systems 2025年第4期31卷 343-350页

作者： Jang, Soyoung Lee, Yeon Woo Jung, Yuna An, Eun Hyeon Lee, Jaeho Department of Information & Communication Technology Convergence Engineering Duksung University Korea Republic of Department of Software Duksung Universitys Korea Republic of

This study introduces an integrated real-time monitoring system to enhance driver safety. The system incorporates facial recognition, alcohol detection, and drowsiness monitoring to comprehensively analyze the driver’s condition and provide appropriate responses. For facial recognition, it utilizes Google’s MediaPipe AI framework and a Siamese network model. Alcohol detection is achieved through real-time measurement of the driver’s alcohol level using an MQ3 sensor. Drowsiness monitoring is accomplished through a combination of blink detection using dlib and OpenCV with head-rotation techniques to assess driver fatigue in real time. The proposed system offers customized information to drivers via Android and web interfaces. This research demonstrates the potential of systems that analyze driver safety and respond accordingly in real time. © ICROS 2025.

关键词： Face recognition

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Clustered Reinforcement Learning

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Frontiers of Computer Science 2025年第4期19卷 43-57页

作者： Xiao MA Shen-Yi ZHAO Zhao-Heng YIN Wu-Jun LI National Key Laboratory for Novel Software Technology Department of Computer Science and TechnologyNanjing UniversityNanjing 210023China Department of Electrical Engineering and Computer Sciences University of CaliforniaBerkeleyCA 94720-1770USA

Exploration strategy design is a challenging problem in reinforcement learning(RL),especially when the environment contains a large state space or sparse *** exploration,the agent tries to discover unexplored(novel)areas or high reward(quality)*** existing methods perform exploration by only utilizing the novelty of *** novelty and quality in the neighboring area of the current state have not been well utilized to simultaneously guide the agent’s *** address this problem,this paper proposes a novel RL framework,called clustered reinforcement learning(CRL),for efficient exploration in *** adopts clustering to divide the collected states into several clusters,based on which a bonus reward reflecting both novelty and quality in the neighboring area(cluster)of the current state is given to the *** leverages these bonus rewards to guide the agent to perform efficient ***,CRL can be combined with existing exploration strategies to improve their performance,as the bonus rewards employed by these existing exploration strategies solely capture the novelty of *** on four continuous control tasks and six hard-exploration Atari-2600 games show that our method can outperform other state-of-the-art methods to achieve the best performance.

关键词： deep reinforcement learning exploration count-based method clustering K-means

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A Helmet Detection Algorithm Based on Transformers with Deformable Attention Module

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Chinese Journal of Electronics 2025年第1期34卷 229-241页

作者： Songle Chen Hongbo Sun Yuxin Wu Lei Shang Xiukai Ruan Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Nanjing University of Posts and Telecommunications State Key Lab for Novel Software Technology Nanjing University Institute of Intelligent Locks Wenzhou University

Wearing a helmet is one of the effective measures to protect workers' safety. To address the challenges of severe occlusion, multi-scale, and small target issues in helmet detection, this paper proposes a helmet detection algorithm based on deformable attention transformers. The main contributions of this paper are as follows. A compact end-to-end network architecture for safety helmet detection based on transformers is proposed. It cancels the computationally intensive transformer encoder module in the existing detection transformer(DETR) and uses the transformer decoder module directly on the output of feature extraction for query decoding, which effectively improves the efficiency of helmet detection. A novel feature extraction network named Swin transformer with deformable attention module(DSwin transformer) is proposed. By sparse cross-window attention, it enhances the contextual awareness of multi-scale features extracted by Swin transformer, and keeps high computational efficiency simultaneously. The proposed method generates the query reference points and query embeddings based on the joint prediction probabilities, and selects an appropriate number of decoding feature maps and sparse sampling points for query decoding, which further enhance the inference capability and processing speed. On the benchmark safety-helmet-wearing-dataset(SHWD), the proposed method achieves the average detection accuracy mAP@0.5 of 95.4% with 133.35G floating-point operations per second(FLOPs) and 20 frames per second(FPS), the state-of-the-art method for safety helmet detection.

关键词： Head Accuracy Network architecture Benchmark testing Transformers Feature extraction Safety Decoding Computational efficiency Detection algorithms

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Poisoning The Diffusion: A Simple and Robust Watermarking Method for Audio Generation

Poisoning The Diffusion: A Simple and Robust Watermarking Me...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Tang, Yi School of Software Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350368741

As the capabilities of diffusion-based audio generation models advance, intellectual property protection for both generated audio and model weights becomes important. However, current audio watermarking methods mainly integrate separate encode and decode modules to watermark the generated audio, which not only imposes unnecessary computational overhead, but also neglects crucial protection of model weights. In this paper, we introduce a novel, simple yet robust watermarking algorithm, termed Poisoning The Diffusion(PTD), tailored for diffusion models. Specifically, PTD incorporates a "poison" vector K during the training phase, modifying the noise distribution in the forward process. Subsequently, the "poisoned" diffusion outputs audio with a unique noise distribution that can be quantitatively identified using numerical testing methods, thus effectively serving as an audio watermark. Furthermore, in the reverse process, in the absence of the "antidote" vector (i.e. authorization), the model is unable to perform correct sampling operations, thereby preventing unauthorized use and fine-tuning. Rigorous experimental validation on datasets comprising vocal and musical compositions confirms the efficacy of PTD, underscoring its stability and robustness. © 2025 IEEE.

关键词： audio generation diffusion models watermarking

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Successive Interference Cancellation-Enabled Timely Status Update in Linear Multi-Hop Wireless Networks

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5298-5311页

作者： Han, Xinhui Pan, Haoyuan Wang, Zhaorui Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Future Network of Intelligence Institute School of Science and Engineering Shenzhen518172 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology College of Computer Science and Software Engineering Shenzhen518060 China

We investigate the timely status update in linear multi-hop wireless networks, where a source tries to deliver status update packets to a destination through a sequence of half-duplex relays. Timeliness is measured by the age of information (AoI) metric. Maintaining a low AoI at the destination typically necessitates frequent transmission of update packets from the source. However, high packet transmission frequency in multi-hop scenarios can result in mutual wireless interference at intermediate relays. Specifically, when an intermediate relay receives wireless signals of a new packet from its previous node, simultaneous transmission of an old packet by its subsequent node to the next hop may cause wireless signals to interfere at the intermediate relay, conventionally leading to packet collision. A key motivation to solve this issue is that the intermediate relay has previously received the old packet (which can thus be forwarded to the subsequent node for further relaying). Hence, successive interference cancellation (SIC) can be employed to mitigate interference of the old packet and recover the new packet. This paper designs an SIC-enabled packet relaying scheme tailored to low AoI. Initially focusing on a three-hop network, we subsequently extend our approach to general multi-hop networks. We model the multi-hop relaying scheme using a Markov chain to derive the theoretical average AoI. Theoretical and simulation results indicate that the SIC-enabled packet relaying scheme significantly reduces the average AoI compared to the non-SIC approaches, owing to an increased packet transmission frequency at the source and the effectiveness of SIC techniques at the relays. © 2002-2012 IEEE.

关键词： Markov chains

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DSCR: A Dynamic Secure Clustering Routing Scheme for UANETs Based on Reputation Mechanism

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IEEE Internet of Things Journal 2025年第10期12卷 14109-14123页

作者： Gong, Yinyan Chen, Yuxiang Li, Kuanching Liang, Wei Li, Xiong Wang, Jin Xiang, Yang Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Sanya Research Institute Hunan University of Science and Technology Sanya572024 China School of Computer Science and Engineering Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Swinburne University of Technology School of Software and Electrical Engineering HawthornVIC3122 Australia

In UAV ad hoc networks (UANETs), rapid movement of nodes leads to frequent changes in network topology, increasing the risk of packet loss and affecting data transmission. Furthermore, current research on drone clustering lacks security considerations, which reduces the reliability of data transmission. Due to this, improving the stability and reliability of network data transmission in dynamically changing UANETs remains a challenge. In this work, we propose the reputation mechanism dynamically secure cluster routing scheme (DSCR) for UANETs, a design for cluster head election in UANETs using the evaluated value and reputation value of drones and forms clusters through this reputation mechanism to avoid malicious nodes from interfering with the clusters to improve the security of UANETs. We apply a residual link survival time-based intra- and inter-cluster algorithm based on residual link survival time for data forwarding of nodes, optimizing the dynamic routing strategy and reducing the packet loss rate of nodes. In addition, reinforcement learning is used in UANETs to achieve cluster decisions based on the current network state and cluster dynamics, effectively improving the stability of clusters. Experiments were conducted on the proposed method to verify its efficiency and stability. Compared to the ICRA, RICR, and DCA algorithms, the stability of the cluster is improved by 11.92%, 28.12%, and 75.2%, respectively, and the packet loss rate reduced by 41.48%, 44.17%, and 55.74%, demonstrating that DSCR is a compelling dynamic routing solution applicable to UANETs. © 2014 IEEE.

关键词： Drones

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A sharding blockchain-based UAV system for search and rescue missions

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Frontiers of Computer Science 2025年第3期19卷 103-118页

作者： Xihan ZHANG Jiashuo ZHANG Jianbo GAO Libin XIA Zhi GUAN Hao HU Zhong CHEN School of Computer Science Peking UniversityBeijing 100871China Peking University Chongqing Research Institute of Big Data Chongqing 401329China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China State Key Lab for Novel Software Technology Nanjing UniversityNanjing 210023China

Sharding is a promising technique to tackle the critical weakness of scalability in blockchain-based unmanned aerial vehicle(UAV)search and rescue(SAR)*** breaking up the blockchain network into smaller partitions called shards that run independently and in parallel,shardingbased UAV systems can support a large number of search and rescue UAVs with improved scalability,thereby enhancing the rescue ***,the lack of adaptability and interoperability still hinder the application of sharded blockchain in UAV SAR *** refers to making adjustments to the blockchain towards real-time surrounding situations,while interoperability refers to making cross-shard interactions at the mission *** address the above challenges,we propose a blockchain UAV system for SAR missions based on dynamic sharding *** from the benefits in scalability brought by sharding,our system improves adaptability by dynamically creating configurable and mission-exclusive shards,and improves interoperability by supporting calls between smart contracts that are deployed on different *** implement a prototype of our system based on Quorum,give an analysis of the improved adaptability and interoperability,and conduct experiments to evaluate the *** results show our system can achieve the above goals and overcome the weakness of blockchain-based UAV systems in SAR scenarios.

关键词： blockchain sharding unmanned aerial vehicle search and rescue blockchain interoperability

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Road Defect Detection Model Based on YOLOv8

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2025年第4期52卷 986-994页

作者： Cong, Shengqiang Zhang, Chunna Yu, Yang Yue, Xiaoping Zhao, Jinchi Shen, Yuming School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China University of Science and Technology Liaoning Liaoning Anshan114051 China

The detection of road defects is crucial for ensuring vehicular safety and facilitating the prompt repair of roadway imperfections. Existing YOLOv8-based models face the following issues: extraction capabilities and insufficient feature representation in complex scenarios, slow bounding box regression speed. To address these challenges, we propose the YOLOv8-DSW model. Firstly, we incorporate the Dilation-wise Residual (DWR) module into the C2f module in the neck, improving the detection performance of multi-scale feature maps. Secondly, We incorporate the SENetv2 attention mechanism into the neck of the YOLOv8 model to augment feature expression capabilities. Finally, we introduce Wise-IoU (WIoU) to enhance the speed of bounding box regression. Experimental results indicate that YOLOv8-DSW enhances the mean Average Precision at 50% (mAP50) by 2.5% in comparison to the YOLOv8n, significantly improving detection accuracy and provide valuable method for the development of road defect detection. © (2025.0), (International Association of Engineers). All Rights Reserved.

关键词： Deep learning

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