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学者姓名：林伟
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Abstract ：
为解决无人机航拍图像中小目标实例多、遮挡严重的问题,提出了一种新的小目标检测算法CDCA-YOLOv8.算法在骨干网络中引入了中心注意力机制,在降低计算复杂度的同时提升特征提取能力;结合可变形卷积网络的优势,改进了卷积模块,并设计了基于可变形卷积技术的C2f模块,增强多尺度特征提取.同时设计了基于自适应结构特征融合的检测头,以提高小目标检测的精度.实验结果表明,与YOLOv8n相比,CDCA-YOLOv8在VisDrone2019数据集上将平均精度均值mAP0.5提高了 4.4个百分点,mAP0.5∶0.95提高了 3.1个百分点,展示了更优的小目标检测效果.
Keyword ：
YOLOv8 YOLOv8 小目标识别 小目标识别 无人机图像 无人机图像 特征提取 特征提取
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| GB/T 7714 | 吴诗娇 , 林伟 . 基于CDCA-YOLOv8的无人机图像小目标识别 [J]. | 电子与封装 , 2025 , 25 (1) : 89-95 . |
| MLA | 吴诗娇 等. "基于CDCA-YOLOv8的无人机图像小目标识别" . | 电子与封装 25 . 1 (2025) : 89-95 . |
| APA | 吴诗娇 , 林伟 . 基于CDCA-YOLOv8的无人机图像小目标识别 . | 电子与封装 , 2025 , 25 (1) , 89-95 . |
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输电线路上的异物检测对确保电力系统安全运行至关重要.为了提高输电线异物识别效率,改进了 YOLOv3-Tiny模型.首先在头部网络中,采用深度可分离卷积替代标准卷积、归一化和激活函数结构,分离空间和通道相关性,降低卷积计算量,提高了识别的速度;其次,引入了考虑距离损失、高宽损失的EIoU的损失函数替代原始的损失函数,使得模型找到边界框预测与类别预测之间的最佳点,从而提升算法的检测效果.消融实验验证了这些改进的有效性,结果表明,改进后的模型在保持高精度的同时,检测速率(FPS)提高了 2.02倍,减少了 74.17％的参数量,大幅降低了计算资源需求.该算法在资源受限环境中表现出色,具备实际应用价值.
Keyword ：
YOLOv3-Tiny YOLOv3-Tiny 损失函数 损失函数 目标检测算法 目标检测算法 输电线异物检测 输电线异物检测
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| GB/T 7714 | 徐玲玲 , 林伟 . 基于输电线异物的轻量级目标检测方法研究 [J]. | 电子与封装 , 2024 , 24 (12) : 80-85 . |
| MLA | 徐玲玲 等. "基于输电线异物的轻量级目标检测方法研究" . | 电子与封装 24 . 12 (2024) : 80-85 . |
| APA | 徐玲玲 , 林伟 . 基于输电线异物的轻量级目标检测方法研究 . | 电子与封装 , 2024 , 24 (12) , 80-85 . |
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Floating garbage on sea surface has always been a key issue in the long-term research of environmental pollution. In order to effectively solve the problem of marine garbage pollution, this paper conducts in-depth research on the existing VGG16 network model and proposes an improved lightweight VGG network model. Instead of the fully connected layer, our model uses the global average pooling layer to reduce the number of network parameters, and adds a residual module to the convolution module to improve the accuracy of the model. The experimental results show that the accuracy of the improved lightweight VGG network model is as high as 97.8% in the self-built sea surface waste data set. Compared with the traditional VGG16 network model, the number of parameters is reduced by 98.5% and the calculation amount is reduced by 78.5%, achieving the goal of rapid and accurate classification. © 2023 SPIE.
Keyword ：
Marine pollution Marine pollution Surface waters Surface waters
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| GB/T 7714 | Lin, Weiming , Lin, Wei . Research on sea surface garbage classification algorithm based on improved VGG network [C] . 2023 . |
| MLA | Lin, Weiming 等. "Research on sea surface garbage classification algorithm based on improved VGG network" . (2023) . |
| APA | Lin, Weiming , Lin, Wei . Research on sea surface garbage classification algorithm based on improved VGG network . (2023) . |
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The object detection technology holds paramount significance in realizing autonomous driving and AI-assisted driving systems. Swift and precise object detection is crucial for enhancing the safety of autonomous vehicles. However, for in-vehicle edge computing platforms, colossal models fall short of meeting real-time detection requirements, while lightweight models often compromise on detection accuracy. Addressing this issue, this paper proposes an improved real-time object detection algorithm based on YOLOv5.In the proposed method, we combine the One-Shot Aggregation (OSA) concept with the progressive channel compression idea and introduce GSConv to innovatively propose the GSPCA structure. It aims to improve some of the problems exposed by the original C3 structure, so as to enhance the model efficiency. Secondly, we also apply GSConv to the neck network of YOLOv5 and introduce the Content-Aware ReAssembly of Features (CARAFE) upsampling operator in the FPN structure, which utilizes its spatial perception and large receptive field to improve the quality of the upsampling, thus enhancing the feature fusion performance of the network. Experimental results demonstrate that, compared to the baseline, our proposed model achieves the highest improvement of 5.2% in mAP@0.5:0.95 on the PASCAL VOC dataset, KITTI dataset, and SODA10m dataset. Furthermore, the model's parameter count and computational load are slightly less than those of the original model. © 2023 ACM.
Keyword ：
Autonomous vehicles Autonomous vehicles Object detection Object detection Object recognition Object recognition Signal detection Signal detection Signal sampling Signal sampling
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| GB/T 7714 | Wang, Jiansong , Lin, Wei . YOLOGP: A YOLOv5-Based Lightweight Network for Efficient Vehicle Detection in Autonomous Driving Scenarios [C] . 2023 : 98-105 . |
| MLA | Wang, Jiansong 等. "YOLOGP: A YOLOv5-Based Lightweight Network for Efficient Vehicle Detection in Autonomous Driving Scenarios" . (2023) : 98-105 . |
| APA | Wang, Jiansong , Lin, Wei . YOLOGP: A YOLOv5-Based Lightweight Network for Efficient Vehicle Detection in Autonomous Driving Scenarios . (2023) : 98-105 . |
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Correcting historical corpora in digital version is a crucial task for the historical research, however, scan quality, book layout, visual character similarity can affect the quality of the recognizing. OCR is at the forefront of digitization projects for cultural heritage preservation. The main task is to identify characters from their visual form into their textual representation. In this paper, we propose a model combining recurrent neutral network(RNN) and deep convolutional network(DCNN) to correct OCR transcription errors. The experiment on a historical book corpus in German language shows that the model is very robust in capturing diverse OCR transcription errors greatly. © Published under licence by IOP Publishing Ltd.
Keyword ：
Convolutional neural networks Convolutional neural networks Historic preservation Historic preservation Recurrent neural networks Recurrent neural networks
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| GB/T 7714 | Lin, Wei , Lin, Zhaoquan . Historical Corpora Correlation based on RNN and DCNN [C] . 2021 . |
| MLA | Lin, Wei 等. "Historical Corpora Correlation based on RNN and DCNN" . (2021) . |
| APA | Lin, Wei , Lin, Zhaoquan . Historical Corpora Correlation based on RNN and DCNN . (2021) . |
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针对当前PID控制器参数难以整定和成本功耗过高的现状,以板球系统为例设计了基于嵌入式设备的自适应PID系统.文章首先改进PID算法并对系统进行模型分析,基于该简化模型提出了一种基于参数实时更新的神经网络来提高控制精度,降低控制时间.将该控制网络简化后,移植到嵌入式设备上实现了PID自适应控制.最后,用对比实验验证了该系统的可行性.
Keyword ：
PID控制 PID控制 嵌入式设备 嵌入式设备 板球系统 板球系统 模型分析 模型分析 神经网络 神经网络
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| GB/T 7714 | 黄国伟 , 林伟 . 基于嵌入式设备的自适应PID控制系统设计 [J]. | 电气开关 , 2020 , 58 (1) : 21-25 . |
| MLA | 黄国伟 等. "基于嵌入式设备的自适应PID控制系统设计" . | 电气开关 58 . 1 (2020) : 21-25 . |
| APA | 黄国伟 , 林伟 . 基于嵌入式设备的自适应PID控制系统设计 . | 电气开关 , 2020 , 58 (1) , 21-25 . |
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In view of the current difficulty in setting PID controller parameters and high cost and power consumption, an adaptive PID system based on embedded devices is designed by taking ball on plate system as an example. The article first introduces the PID algorithm and improves the PID algorithm.The model is then analyzed and simplified for further analysis. Based on the simplified model, a neural network based on real-time parameter updating is proposed to improve the control accuracy and reduce the control time. After simplifying the control network, it is transplanted to the embedded device to implement PID adaptive control. Finally, the feasibility of the system is verified by comparative experiments. © 2019 IEEE.
Keyword ：
Adaptive control systems Adaptive control systems Information management Information management Neural networks Neural networks Proportional control systems Proportional control systems Three term control systems Three term control systems
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| GB/T 7714 | Huang, Guowei , Lin, Wei . Design of Adaptive PID Control System Based on Embedded Device [C] . 2019 : 1530-1533 . |
| MLA | Huang, Guowei 等. "Design of Adaptive PID Control System Based on Embedded Device" . (2019) : 1530-1533 . |
| APA | Huang, Guowei , Lin, Wei . Design of Adaptive PID Control System Based on Embedded Device . (2019) : 1530-1533 . |
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In the work of legal judgment prediction, the most common tasks are crime prediction and related law prediction. These works can be regarded as a multi-label text classification task. In order to further improve the accuracy of prediction work, this paper proposes a multi-task deep neural network classification model, named M-AttBLSTM-CNN, which based on integrating TextCNN with Att-BLSTM model to achieve high-precision prediction for crime prediction and related law prediction. Automatically extracting more rich features is the biggest feature of this model, focusing on local features while taking the full-text information into account at the same time. Firstly, according to the characteristics of the two sub-models, We combining the two sub-models in parallel to obtain more feature information. Secondly, the bidirectional LSTM and attention mechanism are also introduced in the model of this paper, which effectively alleviates the model over-fitting problem and further optimizes the model feature selection. Finally, the experiments are carried out on a judicial documents data set, which enjoys a large number of corpora up to 626,600. From experiments, the proposed model has better performance than the state-of-art text classification models including SVM-TFIDF, hierarchical attention network(HAN) and deep pyramid convolutional neural network (DPCNN). © 2019 IEEE.
Keyword ：
Arts computing Arts computing Classification (of information) Classification (of information) Convolutional neural networks Convolutional neural networks Crime Crime Deep neural networks Deep neural networks Forecasting Forecasting Long short-term memory Long short-term memory Support vector machines Support vector machines Text processing Text processing
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| GB/T 7714 | Huang, Deqin , Lin, Wei . A model for legal judgment prediction based on multi-model fusion [C] . 2019 : 892-895 . |
| MLA | Huang, Deqin 等. "A model for legal judgment prediction based on multi-model fusion" . (2019) : 892-895 . |
| APA | Huang, Deqin , Lin, Wei . A model for legal judgment prediction based on multi-model fusion . (2019) : 892-895 . |
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Real-time detection of the pavement environment is an important part of autonomous driving technology. This paper presents a real-time vehicle detection system based on embedded devices. Based on the existing yolov3-tiny neural network structure, this paper proposes a new neural network structure - the YOLO v3-live. Tailoring the network layer structure of YOLOv3-tiny, and quantify the network parameters in the network. Reducing the complexity of computing in embedded devices, making the proposed neural network structure more suitable for embedded devices. The new structure is tested, before quantization the YOLO v3-live's detection precision can achieve 87.79% mAP on the test set, after quantization of network parameters can achieve 69.79% mAP and detection speed can achieve 28 FPS. © 2019 IEEE.
Keyword ：
Deep learning Deep learning Embedded systems Embedded systems Information management Information management Network layers Network layers Neural networks Neural networks Target tracking Target tracking
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| GB/T 7714 | Chen, Shaobin , Lin, Wei . Embedded System Real-Time Vehicle Detection based on Improved YOLO Network [C] . 2019 : 1400-1403 . |
| MLA | Chen, Shaobin 等. "Embedded System Real-Time Vehicle Detection based on Improved YOLO Network" . (2019) : 1400-1403 . |
| APA | Chen, Shaobin , Lin, Wei . Embedded System Real-Time Vehicle Detection based on Improved YOLO Network . (2019) : 1400-1403 . |
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本文提出了结合状态机与自适应匹配算法来实现停车位检测.首先分析停车位的磁场变化特征,接着提取停车位的磁场变化量,再通过自适应匹配算法来调整阈值.根据实验数据统计,该算法能精确判断停车位状态,且准确率也较高.
Keyword ：
停车位检测 停车位检测 地磁传感器 地磁传感器 自适应匹配算法 自适应匹配算法
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| GB/T 7714 | 林渊博 , 姚剑敏 , 林伟 . 基于地磁传感器的停车位检测算法研究 [J]. | 电气开关 , 2019 , 57 (4) : 42-44 . |
| MLA | 林渊博 等. "基于地磁传感器的停车位检测算法研究" . | 电气开关 57 . 4 (2019) : 42-44 . |
| APA | 林渊博 , 姚剑敏 , 林伟 . 基于地磁传感器的停车位检测算法研究 . | 电气开关 , 2019 , 57 (4) , 42-44 . |
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