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Face Age Estimation Based on CSLBP and lightweight convolutional neural network

Computers, Materials & Continua 2021年第11期69卷 2203-2216页

作者： Yang Wang Ying Tian Ou Tian School of Computer Science and Software Engineering University of Science&Technology LiaoningAnshan114051China Medical and Health Sciences University of WollongongNSW2522Australia

As the use of facial attributes continues to expand,research into facial age estimation is also *** face images are easily affected by factors including illumination and occlusion,the age estimation of faces is a challenging *** paper proposes a face age estimation algorithm based on lightweight convolutional neural network in view of the complexity of the environment and the limitations of device computing *** face age estimation based on Soft Stagewise Regression network(SSR-Net)and facial images,this paper employs the Center Symmetric Local Binary Pattern(CSLBP)method to obtain the feature image and then combines the face image and the feature image as network input *** feature images to the convolutional neural network can improve the accuracy as well as increase the network model *** experimental results on IMDB-WIKI and MORPH 2 datasets show that the lightweight convolutional neural network method proposed in this paper reduces model complexity and increases the accuracy of face age estimations.

关键词： Face age estimation lightweight convolutional neural network CSLBP SSR-Net

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Method for detecting 2D grapevine winter pruning location based on thinning algorithm and lightweight convolutional neural network

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International Journal of Agricultural and Biological Engineering 2022年第3期15卷 177-183页

作者： Qinghua Yang Yuhao Yuan Yiqin Chen Yi Xun Key Laboratory of E&M(Zhejiang University of Technology) Ministry of Education&Zhejiang ProvinceHangzhou 310023China

In viticulture,there is an increasing demand for automatic winter grapevine pruning devices,for which detection of pruning location in vineyard images is a necessary task,susceptible to being automated through the use of computer vision *** this study,a novel 2D grapevine winter pruning location detection method was proposed for automatic winter pruning with a Y-shaped cultivation *** method can be divided into the following four ***,the vineyard image was segmented by the threshold two times Red minus Green minus Blue(2R−G−B)channel and S channel;Second,extract the grapevine skeleton by Improved Enhanced Parallel Thinning Algorithm(IEPTA);Third,find the structure of each grapevine by judging the angle and distance relationship between branches;Fourth,obtain the bounding boxes from these grapevines,then pre-trained MobileNetV3_small×0.75 was utilized to classify each bounding box and finally find the pruning *** to the detection experiment result,the method of this study achieved a precision of 98.8%and a recall of 92.3%for bud detection,an accuracy of 83.4%for pruning location detection,and a total time of 0.423 ***,the results indicated that the proposed 2D pruning location detection method had decent robustness as well as high precision that could guide automatic devices to winter prune efficiently.

关键词： grapevine winter pruning lightweight convolutional neural network thinning algorithm detection method

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A lightweight convolutional neural network with Hierarchical Multi-Scale Feature Fusion for Image Classification

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Journal of Computer and Communications 2024年第2期12卷 173-200页

作者： Adama Dembele Ronald Waweru Mwangi Ananda Omutokoh Kube Department of Mathematics Pan African University Institute for Basic Sciences Technology and Innovation Nairobi Kenya Department of Computing School of Computing and Information Technology Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya Department of Mathematics and Actuarial Sciences Kenyatta University Nairobi Kenya

convolutional neural networks (CNNs) are widely used in image classification tasks, but their increasing model size and computation make them challenging to implement on embedded systems with constrained hardware resources. To address this issue, the MobileNetV1 network was developed, which employs depthwise convolution to reduce network complexity. MobileNetV1 employs a stride of 2 in several convolutional layers to decrease the spatial resolution of feature maps, thereby lowering computational costs. However, this stride setting can lead to a loss of spatial information, particularly affecting the detection and representation of smaller objects or finer details in images. To maintain the trade-off between complexity and model performance, a lightweight convolutional neural network with hierarchical multi-scale feature fusion based on the MobileNetV1 network is proposed. The network consists of two main subnetworks. The first subnetwork uses a depthwise dilated separable convolution (DDSC) layer to learn imaging features with fewer parameters, which results in a lightweight and computationally inexpensive network. Furthermore, depthwise dilated convolution in DDSC layer effectively expands the field of view of filters, allowing them to incorporate a larger context. The second subnetwork is a hierarchical multi-scale feature fusion (HMFF) module that uses parallel multi-resolution branches architecture to process the input feature map in order to extract the multi-scale feature information of the input image. Experimental results on the CIFAR-10, Malaria, and KvasirV1 datasets demonstrate that the proposed method is efficient, reducing the network parameters and computational cost by 65.02% and 39.78%, respectively, while maintaining the network performance compared to the MobileNetV1 baseline.

关键词： MobileNet Image Classification lightweight convolutional neural network Depthwise Dilated Separable Convolution Hierarchical Multi-Scale Feature Fusion

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A Hierarchical Scheme for Video-Based Person Re-identification Using lightweight PCANet and Handcrafted LOMO Features

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Chinese Journal of Electronics 2021年第2期30卷 289-295页

作者： LI Youjiao ZHUO Li LI Jiafeng ZHANG Jing Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Faculty of Information Technology College of Micro-electronics Beijing University of Technology College of Computer Science and Technology Shandong University of Technology

A two-level hierarchical scheme for video-based person re-identification(re-id) is presented, with the aim of learning a pedestrian appearance model through more complete walking cycle extraction. Specifically,given a video with consecutive frames, the objective of the first level is to detect the key frame with lightweight convolutional neural network(CNN) of PCANet to reflect the summary of the video content. At the second level, on the basis of the detected key frame, the pedestrian walking cycle is extracted from the long video sequence. Moreover,local features of Local maximal occurrence(LOMO) of the walking cycle are extracted to represent the pedestrian’s appearance information. In contrast to the existing walking-cycle-based person re-id approaches, the proposed scheme relaxes the limit on step number for a walking cycle, thus making it flexible and less affected by noisy frames. Experiments are conducted on two benchmark datasets: PRID 2011 and i LIDS-VID. The experimental results demonstrate that our proposed scheme outperforms the six state-of-art video-based re-id methods, and is more robust to the severe video noises and variations in pose,lighting, and camera viewpoint.

关键词： video surveillance image sequences two-level hierarchical scheme video content local features image representation video-based re-id methods pedestrian walking cycle video signal processing convolutional neural nets local maximal occurrence noisy frames lightweight PCANet complete walking cycle extraction pedestrian appearance information walking-cycle-based person re-id approaches LOMO features lightweight convolutional neural network learning (artificial intelligence) pedestrian appearance model consecutive frames video noises video-based person re-identification iLIDS-VID benchmark dataset LOMO PRID 2011 benchmark dataset video sequence image matching detected key frame object detection

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Shot classification and replay detection for sports video summarization

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Frontiers of Information Technology & Electronic Engineering 2022年第5期23卷 790-800页

作者： Ali JAVED Amen ALI KHAN Department of Software Engineering University of Engineering and TechnologyTaxila 47050Pakistan

Automated analysis of sports video summarization is challenging due to variations in cameras,replay speed,illumination conditions,editing effects,game structure,genre,*** address these challenges,we propose an effective video summarization framework based on shot classification and replay detection for field sports *** shot classification is mandatory to better structure the input video for further processing,i.e.,key events or replay ***,we present a lightweight convolutional neural network based method for shot *** we analyze each shot for replay detection and specifically detect the successive batch of logo transition frames that identify the replay segments from the sports *** this purpose,we propose local octa-pattern features to represent video frames and train the extreme learning machine for classification as replay or non-replay *** proposed framework is robust to variations in cameras,replay speed,shot speed,illumination conditions,game structure,sports genre,broadcasters,logo designs and placement,frame transitions,and editing *** performance of our framework is evaluated on a dataset containing diverse YouTube sports videos of soccer,baseball,and *** results demonstrate that the proposed framework can reliably be used for shot classification and replay detection to summarize field sports videos.

关键词： Extreme learning machine lightweight convolutional neural network Local octa-patterns Shot classification Replay detection Video summarization

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