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学者姓名：林志贤
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针对目前驾驶员疲劳检测算法存在检测过程复杂、参数多、精度低、运行速度慢等问题,提出了一种基于改进YOLOv8n-Pose的轻量级模型.该模型优化了YOLOv8n-Pose的结构.首先,在模型主干网络中,引入Ghost卷积减少模型参数量和不必要的卷积计算.其次,引入Slim-neck融合主干网络提取的不同尺寸特征,加速网络预测计算.同时在颈部网络添加遮挡感知注意力模块(SEAM),强调图像中的人脸区域并弱化背景,改善关键点定位效果.最后,在检测头部分提出一种GNSC-Head结构,引入共享卷积,并将传统卷积的BN层优化成更稳定的GN层,有效节省模型的参数空间和计算资源.实验结果显示,改进后的YOLOv8n-Pose相较于原始算法,mAP@0.5提高了0.9%,参数量和计算量各减少了50%,同时FPS提高了8%,最终的疲劳驾驶识别率达到93.5%.经验证,本文算法在轻量化的同时能够保持较高的检测精度,并且能够有效识别驾驶员状态,为车辆边缘设备的部署提供有力支撑.
Keyword ：
YOLOv8n-Pose YOLOv8n-Pose 注意力机制 注意力机制 深度学习 深度学习 疲劳驾驶检测 疲劳驾驶检测 轻量化 轻量化
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| GB/T 7714 | 蔡忠祺 , 林珊玲 , 林坚普 et al. 基于改进YOLOv8n-Pose的疲劳驾驶检测 [J]. | 液晶与显示 , 2025 , 40 (4) : 617-629 . |
| MLA | 蔡忠祺 et al. "基于改进YOLOv8n-Pose的疲劳驾驶检测" . | 液晶与显示 40 . 4 (2025) : 617-629 . |
| APA | 蔡忠祺 , 林珊玲 , 林坚普 , 吕珊红 , 林志贤 , 郭太良 . 基于改进YOLOv8n-Pose的疲劳驾驶检测 . | 液晶与显示 , 2025 , 40 (4) , 617-629 . |
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针对野生动物数据集样本量小、目标尺度多变所导致的野生动物检测困难以及检测精度低等问题,提出一种基于多尺度上下文提取的小样本野生动物检测(MS-FSWD)算法.首先,通过多尺度上下文提取模块增强模型对不同尺度的野生动物的感知能力,提高检测性能;其次,引入Res2Net作为原型校准模块的强分类网络对分类器输出的分类分数进行校正;然后,在RPN中加入置换注意力机制,增强目标区域的特征图,弱化背景信息;最后,将平衡L1损失作为定位损失函数,提升目标定位性能.实验结果表明,相比DeFRCN算法,MS-FSWD在小样本野生动物数据集FSWA上,1-shot和3-shot检测任务中新类AP50分别提升了9.9%和6.6%;在公共数据集PASCAL VOC上,MS-FSWD最高提升了12.6%.与VFA算法相比,在PASCAL VOC数据集Novel Set 3的10-shot任务中,新类AP50提升了3.3%.
Keyword ：
多尺度上下文提取 多尺度上下文提取 小样本目标检测 小样本目标检测 注意力机制 注意力机制 迁移学习 迁移学习 野生动物检测 野生动物检测
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| GB/T 7714 | 刘珂 , 林珊玲 , 师欣雨 et al. 基于多尺度上下文提取的小样本野生动物检测 [J]. | 液晶与显示 , 2025 , 40 (3) : 516-526 . |
| MLA | 刘珂 et al. "基于多尺度上下文提取的小样本野生动物检测" . | 液晶与显示 40 . 3 (2025) : 516-526 . |
| APA | 刘珂 , 林珊玲 , 师欣雨 , 林坚普 , 吕珊红 , 林志贤 et al. 基于多尺度上下文提取的小样本野生动物检测 . | 液晶与显示 , 2025 , 40 (3) , 516-526 . |
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Inadequate exposure of imaging devices in low-light environments results in a loss of image information, significantly deteriorating the image quality. However, current low-light image enhancement algorithms commonly suffer from issues such as color distortion and loss of fine details and textures. In this paper, we propose a frequency-guided dual-collapse Transformer (FDCFormer) network. First, in response to color distortion after enhancement, we propose a dual-collapse Transformer that effectively aggregates features from both spatial and channel dimensions, thus capturing global information. Subsequently, because relying solely on enhancement in the spatial domain often makes it difficult to preserve fine details and textures, we design multiple mixed residual fast Fourier transform blocks as additional frequency information guidance branches, focusing on local detail information at the image edges. Additionally, we employ an adaptive dual-domain information fusion module that combines spatial domain and frequency domain information to enrich the final output features. Extensive experiments on multiple publicly available datasets demonstrate that our FDCFormer outperforms state-of-the-art methods, exceeding Retinexformer by up to 0.93 dB on average across five paired datasets. We also employ our method as a preprocessing step in dark detection, our method improves mean average precision (mAP) by 1.9% over the baseline model on ExDark dataset, revealing the latent practical values of our method. The corresponding codes will be available at https://github.com/Fly175/FDCFormer.
Keyword ：
Dual-domain fusion Dual-domain fusion Fourier frequency information Fourier frequency information Low-light image enhancement Low-light image enhancement Vision transformer Vision transformer
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| GB/T 7714 | Lin, Jianpu , Lai, Fangwei , Lin, Shanling et al. Frequency-guided dual-collapse Transformer for low-light image enhancement [J]. | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2025 , 142 . |
| MLA | Lin, Jianpu et al. "Frequency-guided dual-collapse Transformer for low-light image enhancement" . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 142 (2025) . |
| APA | Lin, Jianpu , Lai, Fangwei , Lin, Shanling , Lin, Zhixian , Guo, Tailiang . Frequency-guided dual-collapse Transformer for low-light image enhancement . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2025 , 142 . |
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电润湿电子纸采用减色混色系统进行色彩显示,色域较小,容易发生色彩失真,且依赖环境光的漫反射,亮度不足.为解决这些问题,提出一种基于彩色电润湿的色彩空间转换和图像自适应增强算法.该算法将图像从RGB色彩空间转换到HSV空间,并使用CLAHE对饱和度进行均匀分布处理,改善色彩表现.亮度通道通过引导滤波和改进的Retinex算法进行增强,保留细节与边缘信息,使电润湿电子纸在相同光照下依旧保持真实视觉效果.实验结果表明,该算法在PSNR、SSIM、FSIM和FSIMc上分别提高了19%、10.8%、19.19%和19.54%,显著优化电润湿电子纸的显示效果,为其市场化应用提供有力支撑.
Keyword ：
图像增强 图像增强 彩色电润湿电子纸 彩色电润湿电子纸 直方图均衡 直方图均衡 色彩空间变换 色彩空间变换
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| GB/T 7714 | 毛文杰 , 林珊玲 , 林坚普 et al. 基于色彩空间变换的电润湿电子纸色彩校正 [J]. | 光电工程 , 2025 , 52 (2) : 32-45 . |
| MLA | 毛文杰 et al. "基于色彩空间变换的电润湿电子纸色彩校正" . | 光电工程 52 . 2 (2025) : 32-45 . |
| APA | 毛文杰 , 林珊玲 , 林坚普 , 梅婷 , 王廷雨 , 蔡苾芃 et al. 基于色彩空间变换的电润湿电子纸色彩校正 . | 光电工程 , 2025 , 52 (2) , 32-45 . |
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针对当前三维目标检测由于数据增强导致点云和图像无法有效对齐,点与点对齐方法会丢失图像特征以及定位和分类置信度不一致的问题,提出一种多模态融合的三维目标检测方法.首先,采用PointNet++提取点云的特征;采用卷积神经网络提取图像特征;其次,在点云与图像融合阶段,采用语义对齐方法以及图像球特征,实现点云与图像更好的跨模态对齐.同时采用基于注意力的方法来指导点云与图像特征的融合,以获取更可靠的图像特征;最后引入DIoU损失来平衡置信度不一致的问题.实验结果表明:所采用的方法明显优于baseline,在简单、中等和困难任务下,Car类别的mAP达85.6%.
Keyword ：
多模态融合 多模态融合 彩色图像 彩色图像 激光雷达 激光雷达 自动驾驶 自动驾驶
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| GB/T 7714 | 韩路宇 , 林珊玲 , 赵民 et al. 基于球语义多模态融合的三维目标检测 [J]. | 光电子技术 , 2025 , 45 (1) : 75-81 . |
| MLA | 韩路宇 et al. "基于球语义多模态融合的三维目标检测" . | 光电子技术 45 . 1 (2025) : 75-81 . |
| APA | 韩路宇 , 林珊玲 , 赵民 , 林志贤 , 郭太良 . 基于球语义多模态融合的三维目标检测 . | 光电子技术 , 2025 , 45 (1) , 75-81 . |
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Aiming at the problem of low accuracy of the method for solar cell defect detection, a surface defect detection algorithm based on the improved YOLOv5s solar cell is proposed. First, in order to solve the problem of small target defect detection on the cell sheet, the Contextual Transformer Network (CoT) is proposed, which can provide global contextual information for small targets and the model better at predicting small targets. Secondly, by adding CBAM attention to the C3 module in the Head part, the important channels and spatial locations of the input feature maps can be better captured to improve the performance and robustness of the model. Next, the integrity of feature information is ensured by using CARAFE, a lightweight generalized up-sampling operator, to reduce the loss of feature information during up-sampling. Finally, by using WIoU as the bounding box loss function, the accuracy of the regression can be greatly improved and the convergence of model can be achieved quickly. The experimental results show that compared with the original algorithm, the improved YOLOv5s improves the three indicators of Precision, Recall, and mAP@0. 5 by 5. 5%, 4. 1%, and 3. 3% respectively, and the detection speed reaches 76 FPS, which meets the requirements of solar cell defect detection. © 2024, Science Press. All rights reserved.
Keyword ：
CARAFE CARAFE contextual transformer network contextual transformer network loss function loss function solar cell solar cell YOLOv5s YOLOv5s
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| GB/T 7714 | Peng, X. , Lin, S. , Lin, Z. et al. Defect detection algorithm of improved YOLOv5s solar cell [J]. | Chinese Journal of Liquid Crystals and Displays , 2024 , 39 (2) : 237-247 . |
| MLA | Peng, X. et al. "Defect detection algorithm of improved YOLOv5s solar cell" . | Chinese Journal of Liquid Crystals and Displays 39 . 2 (2024) : 237-247 . |
| APA | Peng, X. , Lin, S. , Lin, Z. , Guo, T. . Defect detection algorithm of improved YOLOv5s solar cell . | Chinese Journal of Liquid Crystals and Displays , 2024 , 39 (2) , 237-247 . |
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Reservoir computing has attracted considerable attention due to its low training cost. However, existing neuromorphic hardware, focusing mainly on shallow-reservoir computing, faces challenges in providing adequate spatial and temporal scales characteristic for effective computing. Here, we report an ultra-short channel organic neuromorphic vertical transistor with distributed reservoir states. The carrier dynamics used to map signals are enriched by coupled multivariate physics mechanisms, while the vertical architecture employed greatly increases the feedback intensity of the device. Consequently, the device as a reservoir, effectively mapping sequential signals into distributed reservoir state space with 1152 reservoir states, and the range ratio of temporal and spatial characteristics can simultaneously reach 2640 and 650, respectively. The grouped-reservoir computing based on the device can simultaneously adapt to different spatiotemporal task, achieving recognition accuracy over 94% and prediction correlation over 95%. This work proposes a new strategy for developing high-performance reservoir computing networks. Existing neuromorphic hardware, focusing mainly on shallow-reservoir computing, is challenged in providing adequate spatial and temporal scales characteristic for effective computing. Here, Gao et al. report an ultra-short channel organic neuromorphic vertical transistor with distributed reservoir states.
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| GB/T 7714 | Gao, Changsong , Liu, Di , Xu, Chenhui et al. Toward grouped-reservoir computing: organic neuromorphic vertical transistor with distributed reservoir states for efficient recognition and prediction [J]. | NATURE COMMUNICATIONS , 2024 , 15 (1) . |
| MLA | Gao, Changsong et al. "Toward grouped-reservoir computing: organic neuromorphic vertical transistor with distributed reservoir states for efficient recognition and prediction" . | NATURE COMMUNICATIONS 15 . 1 (2024) . |
| APA | Gao, Changsong , Liu, Di , Xu, Chenhui , Xie, Weidong , Zhang, Xianghong , Bai, Junhua et al. Toward grouped-reservoir computing: organic neuromorphic vertical transistor with distributed reservoir states for efficient recognition and prediction . | NATURE COMMUNICATIONS , 2024 , 15 (1) . |
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针对太阳能电池片缺陷检测方法存在精度低的问题,提出一种基于改进的YOLOv5s太阳能电池片表面缺陷检测算法.首先,为了解决电池片小目标缺陷检测问题,提出了上下文Transformer网络(CoT),可以为小目标提供全局上下文信息,帮助模型更好地预测小目标.其次,将CBAM注意力加入到Head部分的C3模块,能够更好地捕捉输入特征图的重要通道和空间位置,提高模型的性能和鲁棒性.接着,使用轻量级的通用上采样算子CARAFE减少上采样过程中特征信息的损失,保证了特征信息的完整性.最后,使用WIoU作为边界框损失函数,大幅提升了回归的准确性,并且有助于快速实现模型的收敛.实验结果显示,改进后的YOLOv5s相较于原始算法在Precision、Recall、mAP@0.5三个指标上分别提高了5.5%、4.1%、3.3%,检测速度达到了76 FPS,满足太阳能电池片缺陷检测要求.
Keyword ：
CARAFE CARAFE YOLOv5s YOLOv5s 上下文Transformer网络 上下文Transformer网络 太阳能电池片 太阳能电池片 损失函数 损失函数
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| GB/T 7714 | 彭雪玲 , 林珊玲 , 林志贤 et al. 改进的YOLOv5s太阳能电池片缺陷检测算法 [J]. | 液晶与显示 , 2024 , 39 (2) : 237-247 . |
| MLA | 彭雪玲 et al. "改进的YOLOv5s太阳能电池片缺陷检测算法" . | 液晶与显示 39 . 2 (2024) : 237-247 . |
| APA | 彭雪玲 , 林珊玲 , 林志贤 , 郭太良 . 改进的YOLOv5s太阳能电池片缺陷检测算法 . | 液晶与显示 , 2024 , 39 (2) , 237-247 . |
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Organic photoelectric neuromorphic devices that mimic the brain are widely explored for advanced perceptual computing. However, current individual neuromorphic synaptic devices mainly focus on utilizing linear models to process optoelectronic signals, which means that there is a lack of effective response to nonlinear structural information from the real world, severely limiting the computational efficiency and adaptability of networks to static and dynamic information. Here, a feedforward photoadaptive organic neuromorphic transistor with mixed-weight plasticity is reported. By introducing the potential of the space charge to couple gate potential, photoexcitation, and photoinhibition occur successively in the channel under the interference of constant light intensity, which enables the device to transform from a linear model to a nonlinear model. As a result, the device exhibits a dynamic range of over 100 dB, exceeding the currently reported similar neuromorphic synaptic devices. Further, the device achieves adaptive tone mapping within 5 s for static information and achieves over 90% robustness recognition accuracy for dynamic information. Therefore, this work provides a new strategy for developing advanced neuromorphic devices and has great potential in the fields of intelligent driving and brain-like computing. This work proposes a feedforward adaptive organic neuromorphic transistor with mixed-weight plasticity (MP-ONH) that transitions from linear mode to nonlinear mode based on light intensity. It achieves a dynamic range of 100 dB for light intensity, surpassing current similar synaptic devices. It also enables adaptive tone mapping in 5 s for static information and achieves over 90% accuracy in robust recognition of dynamic information.image
Keyword ：
artificial synapse artificial synapse neuromorphic computing neuromorphic computing organic semiconductor heterojunction organic semiconductor heterojunction photoelectric adaptation photoelectric adaptation phototransistor phototransistor
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| GB/T 7714 | Gao, Changsong , Liu, Di , Xu, Chenhui et al. Feedforward Photoadaptive Organic Neuromorphic Transistor with Mixed-Weight Plasticity for Augmenting Perception [J]. | ADVANCED FUNCTIONAL MATERIALS , 2024 , 34 (18) . |
| MLA | Gao, Changsong et al. "Feedforward Photoadaptive Organic Neuromorphic Transistor with Mixed-Weight Plasticity for Augmenting Perception" . | ADVANCED FUNCTIONAL MATERIALS 34 . 18 (2024) . |
| APA | Gao, Changsong , Liu, Di , Xu, Chenhui , Bai, Junhua , Li, Enlong , Zhang, Xianghong et al. Feedforward Photoadaptive Organic Neuromorphic Transistor with Mixed-Weight Plasticity for Augmenting Perception . | ADVANCED FUNCTIONAL MATERIALS , 2024 , 34 (18) . |
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In order to solve the problem of low gray level due to the few driving chips developed based on the photoelectric characteristics of electrowetting display, a driving method based on modulation is proposed to double the gray level of electrowetting display. In this method, the driving waveform based on pulse amplitude modulation and pulse width modulation hybrid modulation is designed, and the gray level-luminance curve of the electrowetting display is measured and analyzed. On this basis, the luminance nonlinear correction is carried out, and the improvement of 64 Gy levels to 127 Gy levels is realized by the principle of human visual persistence phenomenon. The experimental results show that the proposed driving scheme can break through the limitation of the driving chip and realize the multiplication of gray levels, in which 96% gray levels increase steadily with an average luminance difference of 0.07, and at the same time enhance the contrast and improve the display effect.
Keyword ：
driving waveform driving waveform electrowetting display electrowetting display gray scale gray scale hybrid modulation hybrid modulation nonlinearity nonlinearity PAM PAM PWM PWM
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| GB/T 7714 | Mei, Ting , Lin, Zhixian , Xie, Ziyu et al. A driving method for gray scale multiplication of electrowetting display based on hybrid modulation [J]. | FRONTIERS IN PHYSICS , 2024 , 12 . |
| MLA | Mei, Ting et al. "A driving method for gray scale multiplication of electrowetting display based on hybrid modulation" . | FRONTIERS IN PHYSICS 12 (2024) . |
| APA | Mei, Ting , Lin, Zhixian , Xie, Ziyu , Lin, Shanling , Cai, Bipeng , Chen, Mingzhen et al. A driving method for gray scale multiplication of electrowetting display based on hybrid modulation . | FRONTIERS IN PHYSICS , 2024 , 12 . |
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