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Chromatic Aberration Identification of Fair-Faced Concrete Research Based on Multi-Scale lightweight Structured Data algorithm

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FRONTIERS IN MATERIALS 2022年 9卷

作者： Yao, Gang Sun, Wentong Yang, Yang Sun, Yujia Xu, Liangjin Zhou, Jian Chongqing Univ Sch Civil Engn Chongqing Peoples R China Minist Educ Key Lab New Technol Construction Cities Mt Area Chongqing Peoples R China

Chromatic aberration is one of the quality defects in the appearance of fair-faced concrete (FFC). The mainly surface chromatic aberration identification (CAI) method being applied is manual observation, which is subjective and time-consuming. A multi-scale lightweight structured data algorithm (MSLSDA) for CAI in FFC is proposed in this manuscript. An unmanned aerial vehicle (UAV) is used for image acquisition. 2368 FFC sample images are collected to build the datasets. The FFC chromatic aberration features are identified by the improved Residual Network Convolutional Neural Network (CNN) framework to achieve chromatic aberration samples quantitative analysis. The method proposed in this manuscript can verify the generalization prediction ability of the MSLSDA for different building samples by generalization prediction set. The results show that the accuracy in CAI samples and chromatic aberration generalization prediction samples can achieve 92.1 and 99.6%, respectively. The FFC chromatic aberration detection platform (FFC-CADP) built by color space conversion, histogram equalization, image color recognition, image noise reduction and image mask algorithm is able to calculate boundary features, geometric parameter features (length and width), chromatic aberration ratio features, total chromatic aberration ratio and number of chromatic aberration.

关键词： multi-scale feature fusion structured data algorithm fair-faced concrete chromatic aberration algorithm hyperparameter optimization lightweight algorithm

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LDCA: lightweight Dynamic Clustering algorithm for IoT-Connected Wide-Area WSN and Mobile Data Sink Using LoRa

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IEEE INTERNET OF THINGS JOURNAL 2022年第2期9卷 1313-1325页

作者： Rahman, Gazi M. E. Wahid, Khan A. Univ Saskatchewan Dept Elect & Comp Engn Saskatoon SK S7N 5A9 Canada

Wide-area monitoring applications of the Internet-of-Things (IoT) connected wireless sensor network (WSN) consists of sensor nodes (SNs) with limited hardware and energy sources. The distributed nature of such a network and the difficulty of remote access make it more demanding to design an energy-efficient WSN. Moreover, long-range and low-power wireless connectivity is a challenge in IoT-connected applications. Present WSN topologies deal mainly with fixed SNs, SN distribution, and fixed data sink (DS). The majority of the control layer is implemented in the lower hierarchical layer or in a virtual middle layer, which reduces the network lifetime due to excessive processing and data transmission activities. This article proposes a real-time lightweight dynamic clustering algorithm (LDCA) for a WSN that supports the following two scenarios with limited processing resources: 1) with mobile DSs and static SNs (such as DS and SNs mounted on unmanned aerial vehicles, autonomous vehicles) and 2) with mobile SNs and static DSs (such as livestock monitoring or autonomous robots in smart farming, and urban monitoring). The proposed algorithm is based on the received signal strength indicator and signal-to-noise ratio of a long-range (LoRa) interface and its residual energy. Mathematical models were derived for real-time clustering using LoRa. Memory requirement and clustering efficiency of the constrained SN and DS for various mobility scenario were evaluated. The proposed LDCA reduces the energy requirement to 33% compared to static clustering, by reducing the number of concurrent clusters and hops. In addition, a hardware-based approach was used to validate the LDCA algorithm, and evaluate its performance in terms of energy efficiency, packet delivery rate, and network lifetime.

关键词： Wireless sensor networks Heuristic algorithms Clustering algorithms Monitoring Wireless communication Topology Network topology Dynamic clustering Internet of Things (IoT) lightweight algorithm long-range (LoRa) mobile sensor node (SN) mobile sink wireless sensor network (WSN)

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GST-YOLO: a lightweight visual detection algorithm for underwater garbage detection

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2024年第4期21卷 114-114页

作者： Jiang, Longyi Liu, Fanghua Lv, Junwei Liu, Binghua Wang, Chen Jiangsu Univ Sci & Technol Fac Mech Engn Zhenjiang 212100 Peoples R China

Underwater cleaning work primarily relies on human labor, but applying computer vision technology to Autonomous Underwater Vehicles can enhance cleaning efficiency. Considering that existing vision detection algorithms are difficult to deploy on resource-constrained embedded devices, this paper introduces a lightweight vision detection algorithm based on an improved YOLOv8-GST-YOLO. This algorithm integrates the lightweight Ghost network and prunes the model, overcoming the drawbacks of YOLOv8's high computational parameters and large size. It also features a GTR module and a bi-directional path aggregated feature pyramid guided by SimAM attention to enhance detection accuracy and global feature extraction capabilities. Experiments on a specially collected underwater trash image dataset show that GST-YOLO, while reducing the model size by 51% and increasing computational efficiency by 54%, improves the accuracy rate to 95.4%, surpassing the YOLOv8 algorithm. This demonstrates its potential as a crucial detection tool for underwater unmanned cleaning tasks, offering broad application prospects.

关键词： Underwater debris Visual detection YOLOv8 lightweight algorithm

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YOLO-FAS: A lightweight model for detecting rebar intersections location and tying status

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NEUROCOMPUTING 2025年 624卷

作者： Duan, Hao Yu, Mingming Ai, Tengfeng Zhu, Mengmeng Jiang, Haili Guo, Shuai Shanghai Univ Sch Mechatron Engn & Automat Shanghai Key Lab Intelligent Mfg & Robot Shanghai 200444 Peoples R China Shanghai Rd & Bridge Grp Co Ltd Shanghai 200444 Peoples R China

Current deep learning (DL) algorithms for detecting complex rebar mesh intersection points rely heavily on large amounts of training data to ensure recognition accuracy and generalization capabilities. However, deploying such algorithms on small mobile robots is challenging due to limited computational resources, making it difficult to achieve both real-time performance and high precision. To address this issue, this paper proposes a lightweight YOLO-FAS model specifically designed for rebar intersections detection on tying robots. This model is based on YOLOv5s and optimizes the FastNet structure by introducing the PGConv module, enhances feature fusion with the AFPN (Asymptotic feature pyramid network) module, and improves localization accuracy using the EIOU loss function. Experimental results demonstrate that the model's parameters and computational load are reduced by 60.6 % and 62 %, respectively. Furthermore, through BN (Batch Normalization) channel pruning and QAT (Quantization Aware Training), the model is compressed, and inference is accelerated using TensorRT. The improved YOLO-FAS model achieves a FPS (frames per second) increase of 84.1 % in FP32 mode and 33.2 % in INT8 mode. Finally, after real-world deployment testing, the system's average memory usage is reduced by 0.77 GB, and the accuracy of recognition of intersection points reaches 98.21 %, representing an improvement of 3.04 % over YOLOv5s. The results indicate that this method effectively achieves model lightweighting while ensuring efficient and accurate detection of rebar intersection points, demonstrating robust performance and promising application prospects.

关键词： Rebar intersections detection lightweight algorithm YOLO-FAS Model compression TensorRT

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A lightweight deep learning method to identify different types of cervical cancer

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SCIENTIFIC REPORTS 2024年第1期14卷 1-17页

作者： Mehedi, Md. Humaion Kabir Khandaker, Moumita Ara, Shaneen Alam, Md. Ashraful Mridha, M. F. Aung, Zeyar Brac Univ Dept Comp Sci & Engn Dhaka Bangladesh Bangladesh Univ Business & Technol Dept Comp Sci & Engn Dhaka Bangladesh Amer Int Univ Bangladesh Dept Comp Sci Dhaka Bangladesh Khalifa Univ Sci & Technol Dept Comp Sci Abu Dhabi U Arab Emirates

Cervical cancer is the second most common cancer in women's bodies after breast cancer. Cervical cancer develops from dysplasia or cervical intraepithelial neoplasm (CIN), the early stage of the disease, and is characterized by the aberrant growth of cells in the cervix lining. It is primarily caused by Human Papillomavirus (HPV) infection, which spreads through sexual activity. This study focuses on detecting cervical cancer types efficiently using a novel lightweight deep learning model named CCanNet, which combines squeeze block, residual blocks, and skip layer connections. SipakMed, which is not only popular but also publicly available dataset, was used in this study. We conducted a comparative analysis between several transfer learning and transformer models such as VGG19, VGG16, MobileNetV2, AlexNet, ConvNeXT, DeiT_tiny, MobileViT, and Swin Transformer with the proposed CCanNet. Our proposed model outperformed other state-of-the-art models, with 98.53% accuracy and the lowest number of parameters, which is 1,274,663. In addition, accuracy, precision, recall, and the F1 score were used to evaluate the performance of the models. Finally, explainable AI (XAI) was applied to analyze the performance of CCanNet and ensure the results were trustworthy.

关键词： Cervical cancer Cancer type identification Deep learning lightweight algorithm

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lightweight LUW-DETR for efficient underwater benthic organism detection

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VISUAL COMPUTER 2025年 1-16页

作者： Li, Ning Ding, Bocheng Yang, Gaochao Ni, Shiyun Wang, Mingliang Changzhou Univ Sch Comp Sci & Artificial Intelligence Aliyun Sch Big Data Sch Software Changzhou 213164 Peoples R China

With the advancement of marine science research, the study and monitoring of benthic organisms have gained prominence. These organisms, inhabiting the seafloor, play crucial roles in marine ecosystems. However, traditional methods for benthic organism surveys rely heavily on manual operations, which are labor-intensive and prone to errors. Moreover, the complexity of underwater environments poses significant challenges for automated detection algorithms. Current models face issues such as large parameter sizes, high computational demands, and difficulties in detecting small targets and overlapping objects. To address these challenges, we propose LUW-DETR, a lightweight algorithm based on the transformer architecture. LUW-DETR incorporates a high-low-frequency attention mechanism and dynamic group convolution module to significantly improve feature extraction and computational efficiency. Experimental results demonstrate that LUW-DETR achieves competitive performance with an mAP of 83.1% on the URPC 2020 dataset and 63.7% on the Pascal VOC 2007 dataset, while reducing parameters by 28.3% and computational cost by 29.6% compared to baseline models. These results highlight LUW-DETR's balance between accuracy and efficiency, making it suitable for resource-constrained underwater environments. Our source code is available at:https://***/DingBC/LUWDETR.

关键词： Underwater benthic organism detection lightweight algorithm LUW-DETR

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A lightweight segmentation model toward timely processing for identification of pine wood nematode affected trees with UAV

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PLANT METHODS 2025年第1期21卷 1-16页

作者： Wu, Qiangjia Chen, Meixiang Shi, Hao Yi, Tongchuan Xu, Gang Wang, Weijia Zhang, Ruirui Beijing Acad Agr & Forestry Sci Intelligent Equipment Res Ctr Beijing 100097 Peoples R China Northwest A&F Univ Coll Informat Engn Yangling 712100 Peoples R China

Pine wood nematode (PWN), a major international quarantined forest pest, has resulted in significant loss to the pine forest resources, posing a serious threat to global forest ecosystems. Quick and accurate identification of trees infected by PWN can lead to earlier intervention in their spread, thereby significantly reducing losses. However, there is a scarcity of algorithm that are both swift and precise. To achieve more rapid and precise segmentation of trees affected by PWN, we proposed a novel lightweight model termed Refined and Deformable Carafe Attention Net (RCANet). The RCANet excels in both accuracy and real-time performance. It has achieved segmentation accuracy that surpasses mainstream segmentation networks, including DeepLabv3 + , Segformer, PSPNet, HrNet, and UNet. The number of parameters in RCANet is only 5.373 million, the segmentation speed reached 83.14 fps. Compared to the baseline model UNet, the IoU of the affected trees class is improved by 5.6%, and the segmentation speed is accelerated by about 90%. A straightforward yet highly effective lightweight structure was proposed, termed Refined VGG. Additionally, we validate the efficacy of several network modules for this task. RCANet addressed the challenges of low accuracy and inadequate real-time capabilities in the identification of PWN-affected pine trees within intricate forest landscapes. which is expected to be deployed on UAVs in the future for real-time recognition to accelerate the identification and localization of affected trees. This work could facilitate the management of PWN.

关键词： Pine wood nematode Pine wilt disease Semantic segmentation lightweight algorithm UAV remote sensing

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DRViT: A dynamic redundancy-aware vision transformer accelerator via algorithm and architecture co-design on FPGA

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2025年 199卷

作者： Sun, Xiangfeng Zhang, Yuanting Wang, Qinyu Zou, Xiaofeng Liu, Yujia Zeng, Ziqian Zhuang, Huiping South China Univ Technol Guangzhou 511442 Peoples R China Guangdong Univ Technol Guangzhou 510006 Peoples R China

The multi-modal artificial intelligence (MAI) has attracted significant interest due to its capability to process and integrate data from multiple modalities, including images, text, and audio. Addressing MAI tasks in distributed systems necessitate robust and efficient architectures. The Transformer architecture has emerged as a primary network in this context. The integration of Vision Transformers (ViTs) within multimodal frameworks is crucial for enhancing the processing and comprehension of image data across diverse modalities. However, the complex architecture of ViTs and the extensive resources required for processing large-scale image data pose high computational and storage demands. These demands are particularly challenging for deploying ViTs on edge devices within distributed frameworks. To address this issue, we propose a novel dynamic redundancy-aware ViT accelerator based on parallel computing, termed DRViT. DRViT is supported by an algorithm and architecture co- design. We first propose a hardware-friendly lightweight algorithm featuring token merging, token pruning, and an INT8 quantization scheme. Then, we design a specialized architecture to support this algorithm, transforming the lightweight algorithm into significant latency and energy-efficiency improvements. Our design is implemented on the Xilinx Alveo U250, achieving an overall inference latency of 0.86 ms and 1.17 ms per image for ViT-tiny at 140 MHz and 100 MHz, respectively. The throughput can reach 1,380 GOP/s at peak, demonstrating superior performance compared to state-of-the-art accelerators, even at lower frequencies.

关键词： Multi-modal artificial intelligence (MAI) Vision Transformer (ViT) Field-programmable gate array (FPGA) lightweight algorithm algorithm and architecture co-design

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Modified lightweight cryptography scheme and its applications in IoT environment

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International Journal of Information Technology (Singapore) 2023年第8期15卷 4403-4414页

作者： Yasmin, Nilufar Gupta, Richa Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Uttar Pradesh Noida India

Interconnected devices have been with us for a long time. The security of the data exchanged through these devices is a matter of grave concern. While exchanging information, a secure network creates trust among parties participating in e-commerce. lightweight cryptography deals with algorithms specially designed for providing security to resource-constrained devices. This paper represents a scheme to encrypt files in an energy-efficient manner as suited for the Internet of Things (IoT) environment. Among various block ciphers and stream ciphers available;modified lightweight block cipher GIFT is tested. As per the proposed scheme a modified version of GIFT with bit-slice substitution and involutive permutation is used for data encryption. Encryption and related simulation are carried out using MATLAB tool. Some of the famous lightweight algorithms like GIFT-KSA, GIFT-COFB, PRESENT, PRESENT-KSA, SIMON, and SKINNY are used for comparison based on the state-of-the-art parameters like computation time, power, energy, latency, throughput, and software efficiency. One of the applications in the IoT environment is demonstrated for better understanding. Modified GIFT stands out to be a super-efficient solution for providing security for various IoT applications. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Encryption GIFT IoT lightweight algorithm Security

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High-throughput and area-efficient architectures for image encryption using PRINCE cipher

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INTEGRATION-THE VLSI JOURNAL 2023年第1期90卷 224-235页

作者： Kumar, Abhiram Singh, Pulkit Patro, K. Abhimanyu Kumar Acharya, Bibhudendra Natl Inst Technol Raipur Dept Elect & Commun Engn GE Rd Raipur 492010 Chhattisgarh India MLR Inst Technol Dept Elect & Commun Engn Hyderabad 500043 India Manipal Acad Higher Educ Manipal Inst Technol Dept Mechatron Manipal 576104 Karnataka India

Internet of Things (IoT) has gained popularity in recent years and has engulfed nearly every industry. The widespread use of numerous ubiquitous computing devices in the low-resource domain has resulted in a new set of privacy and security concerns. To address the problem of security in resource-constrained devices, many lightweight algorithms have been developed. This paper proposes optimized hardware implementations of the lightweight PRINCE block cipher, with the aim of providing adequate security while maximizing resource effi-ciency. The proposed architecture uses fewer resources and provides a reasonable trade-off between area foot-print and efficiency. In the proposed unrolled pipelined architecture, the encryption round is divided into three sub-stages, with registers inserted in between. Using this design approach, the operating frequency is greatly improved. As a result, this architecture adapts itself effectively to high-performance applications. This paper also proposes serial-based and round-based architectures for resource-constrained devices. The proposed unrolled sub pipeline PRINCE block cipher is implemented on the Virtex-6-FF784 and Virtex-4-FF668 FPGA device families and achieved substantial improvements in throughput of 13.057% and 113%, respectively, as well as efficiency of 8.109% and 113.734% respectively. The proposed architecture is evaluated on a variety of grayscale images, and the security analysis is performed using MATLAB software. Aside from that, the proposed architecture uses the CBC-mode of operation. The security analysis and encryption outputs show that the proposed architecture is an effective choice for image encryption and provides sufficient security to the cipher images.

关键词： Security lightweight algorithm PRINCE cipher CBC mode FPGA Hardware architecture

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