2023 18th International Conference on Machine Vision and Applications (MVA)最新文献_第3页

A Hybrid Wheat Head Detection model with Incorporated CNN and Transformer 结合CNN和变压器的杂交小麦抽穗检测模型

2023 18th International Conference on Machine Vision and Applications (MVA) Pub Date : 2023-07-23 DOI: 10.23919/MVA57639.2023.10216087

Shou Harada, Xian-Hua Han

{"title":"A Hybrid Wheat Head Detection model with Incorporated CNN and Transformer","authors":"Shou Harada, Xian-Hua Han","doi":"10.23919/MVA57639.2023.10216087","DOIUrl":"https://doi.org/10.23919/MVA57639.2023.10216087","url":null,"abstract":"Wheat head detection is an important research topic for production estimation and growth management. Motivated by the great advantages of the deep convolution neural networks (DCNNs) in many vision tasks, the deep-learning based methods have dominated the wheat head detection field, and manifest remarkable performance improvement compared with the traditional image processing methods. The existing methods usually divert the proposed detection models for the generic object detection to wheat head detection, and are insuﬃcient in taking account of the specific characteristics of the wheat head images such as large variations due to different growth stages, high density and overlaps. This work exploits a novel hybrid wheat detection model by incorporating the CNN and transformer for modeling long-range dependence. Specifically, we firstly employ a backbone ResNet to extract multi-scale features, and leverage an inter-scale feature fusion module to aggregate coarse-to-fine features together for capturing suﬃcient spatial detail to localize small-size wheat head. Moreover, we propose a novel and eﬃcient transformer block by incorporating the self-attention module in channel direction and the feature feed-forward subnet to explore the interaction among the aggregated multi-scale features. Finally a prediction head produces the centerness and size of wheat heads to obtain a simple anchor-free detection model. Extensive experiments on the Global Wheat Head Detection (GWHD) dataset have demonstrated the superiority of our proposed model over the existing state-of-the-art methods as well as the baseline model.","PeriodicalId":338734,"journal":{"name":"2023 18th International Conference on Machine Vision and Applications (MVA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126063810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quadruped Robot Platform for Selective Pesticide Spraying 四足机器人农药选择性喷洒平台

2023 18th International Conference on Machine Vision and Applications (MVA) Pub Date : 2023-07-23 DOI: 10.23919/MVA57639.2023.10215812

Hansen Hendra, Yubin Liu, Ryoichi Ishikawa, Takeshi Oishi, Yoshihiro Sato

引用次数: 0

Dynamic Transfer for Domain Adaptation in Crowd Counting 群体计数中领域自适应的动态传递

2023 18th International Conference on Machine Vision and Applications (MVA) Pub Date : 2023-07-23 DOI: 10.23919/MVA57639.2023.10216197

Shekhor Chanda, Yang Wang

引用次数: 0

Human Pose Prediction by Progressive Generation in Multi-scale Frequency Domain 基于多尺度频域渐进生成的人体姿态预测

2023 18th International Conference on Machine Vision and Applications (MVA) Pub Date : 2023-07-23 DOI: 10.23919/MVA57639.2023.10215966

Tomohiro Fujita, Yasutomo Kawanishi

引用次数: 0

Towards Achieving Lightweight Deep Neural Network for Precision Agriculture with Maize Disease Detection 基于玉米病害检测的精准农业轻量深度神经网络研究

2023 18th International Conference on Machine Vision and Applications (MVA) Pub Date : 2023-07-23 DOI: 10.23919/MVA57639.2023.10215815

C. Padeiro, Takahiro Komamizu, I. Ide

{"title":"Towards Achieving Lightweight Deep Neural Network for Precision Agriculture with Maize Disease Detection","authors":"C. Padeiro, Takahiro Komamizu, I. Ide","doi":"10.23919/MVA57639.2023.10215815","DOIUrl":"https://doi.org/10.23919/MVA57639.2023.10215815","url":null,"abstract":"Agriculture is the pillar industry of human survival. However, various crop diseases reduce the human food supply and lead to starvation and death in the worst cases. Experts perform visual symptoms observation for crop disease diagnosis. Which process is time-consuming and expensive. Also, the process has significant risk of human error due to subjective perception. Convolutional Neural Networks (CNN) use image processing techniques to show great potential in plant disease detection. However, it requires thousands of channels to learn rich features, resulting in large models requiring powerful computing, power supply, and high bandwidth, making it more expensive and difficult for farmers to acquire. Therefore, deploying these solutions on resource-constrained devices is desirable to make them more accessible. Thus, we propose a lightweight object detection CNN that can run on resource-constrained devices to detect crop diseases. Channel pruning is applied to optimize resource use by removing unimportant channels and filter weights to reduce network parameters, inference time, and the number of FLOPS. Experimental results with object detector, Faster R-CNN with two backbones, ResNet-50, and EfficientNet-B7, show significant improvement in model efficiency, keeping high accuracy.","PeriodicalId":338734,"journal":{"name":"2023 18th International Conference on Machine Vision and Applications (MVA)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114542446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MVA 2023 Cover Page MVA 2023封面

2023 18th International Conference on Machine Vision and Applications (MVA) Pub Date : 2023-07-23 DOI: 10.23919/mva57639.2023.10216272

引用次数: 0

Diabetic Retinopathy Grading based on a Sparse Network Fusion of Heterogeneous ConvNeXt Models with Category Attention 基于类别关注的异构ConvNeXt模型稀疏网络融合的糖尿病视网膜病变分级

2023 18th International Conference on Machine Vision and Applications (MVA) Pub Date : 2023-07-23 DOI: 10.23919/MVA57639.2023.10216129

Agustin Castillo-Munguia, Gibran Benitez-Garcia, J. Olivares-Mercado, Hiroki Takahashi

{"title":"Diabetic Retinopathy Grading based on a Sparse Network Fusion of Heterogeneous ConvNeXt Models with Category Attention","authors":"Agustin Castillo-Munguia, Gibran Benitez-Garcia, J. Olivares-Mercado, Hiroki Takahashi","doi":"10.23919/MVA57639.2023.10216129","DOIUrl":"https://doi.org/10.23919/MVA57639.2023.10216129","url":null,"abstract":"Diabetic retinopathy (DR) is an eye disease caused by high blood sugar levels that may damage vessels in the retina, leading to partial or complete loss of vision in later stages. In recent years, convolutional neural networks (CNN) have been used to help diagnose the DR severity. However, due to the slight differences between each class and the imbalanced nature of the datasets, standard CNNs often struggle to distinguish accurately between different grades of DR. To overcome these challenges, we propose combining a novel CNN model (ConvNeXt) with category-attention blocks incorporated at multiple levels of the architecture. This generates different models that can effectively extract fine-grained features and minimize the impact of dataset imbalance. Finally, we introduce a Sparse Network Fusion technique that learns to combine the outputs of all models to consolidate their individual decisions. Extensive experiments on the challenging DDR dataset show that our proposal achieves a new state-of-the-art performance, improving by about 3% grading accuracy compared with existing methods.","PeriodicalId":338734,"journal":{"name":"2023 18th International Conference on Machine Vision and Applications (MVA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114482554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Object Detection for Embedded Systems Using Tiny Spiking Neural Networks: Filtering Noise Through Visual Attention 基于微脉冲神经网络的嵌入式系统目标检测:通过视觉注意过滤噪声

2023 18th International Conference on Machine Vision and Applications (MVA) Pub Date : 2023-07-23 DOI: 10.23919/MVA57639.2023.10215590

Hugo Bulzomi, Amélie Gruel, Jean Martinet, Takeshi Fujita, Yuta Nakano, R. Bendahan

{"title":"Object Detection for Embedded Systems Using Tiny Spiking Neural Networks: Filtering Noise Through Visual Attention","authors":"Hugo Bulzomi, Amélie Gruel, Jean Martinet, Takeshi Fujita, Yuta Nakano, R. Bendahan","doi":"10.23919/MVA57639.2023.10215590","DOIUrl":"https://doi.org/10.23919/MVA57639.2023.10215590","url":null,"abstract":"Object detection is an important task becoming increasingly common in numerous applications for embedded systems. The traditional state-of-the-art deep neural networks (DNNs) tend to be incompatible with the limitations of many of those systems: their large size and high computational cost make them hard to deploy on hardware with limited resources. Spiking Neural Networks (SNNs) have been attracting attention in recent years because of their potential as energy-efficient alternatives when implemented on specialized hardware, and their smooth integration with energy-efficient event cameras. In this paper, we present a lightweight SNN architecture for efficient object detection in embedded systems using event camera data. We show that by applying visual attention mechanisms, we can ignore most of the noise from the input and thus reduce the number of neurons and activations since additional noise-filtering layers are not needed. Our proposed SNN is 24 times smaller than a previous similar method for our input resolution and maintains similar overall detection performances, while being more robust to noise. We finally demonstrate the energy efficiency of our network during runtime with an implementation on SpiNNaker chip, showing the applicability of our approach.","PeriodicalId":338734,"journal":{"name":"2023 18th International Conference on Machine Vision and Applications (MVA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129331898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Combining Static Specular Flow and Highlight with Deep Features for Specular Surface Detection 结合静态高光流和高光与深度特征的高光表面检测

2023 18th International Conference on Machine Vision and Applications (MVA) Pub Date : 2023-07-23 DOI: 10.23919/mva57639.2023.10215694

Hirotaka Hachiya, Yuto Yoshimura

引用次数: 0

Shape Preservation in Image Style Transfer for Gaze Estimation 注视估计中图像样式转移中的形状保持

2023 18th International Conference on Machine Vision and Applications (MVA) Pub Date : 2023-07-23 DOI: 10.23919/MVA57639.2023.10216216

Daiki Mushiake, Kentaro Otomo, Chihiro Nakatani, N. Ukita

引用次数: 0