Proceedings of the 5th International Conference on Control and Computer Vision最新文献

Fish Target Detection Method Based on EPSA-CenterNet2 基于EPSA-CenterNet2的鱼类目标检测方法

Proceedings of the 5th International Conference on Control and Computer Vision Pub Date : 2022-08-19 DOI: 10.1145/3561613.3561620

Zetao Hu, Haitao Li, Junhu Zhang, Dechun Zhang, Meiling Su

{"title":"Fish Target Detection Method Based on EPSA-CenterNet2","authors":"Zetao Hu, Haitao Li, Junhu Zhang, Dechun Zhang, Meiling Su","doi":"10.1145/3561613.3561620","DOIUrl":"https://doi.org/10.1145/3561613.3561620","url":null,"abstract":"At present, small target detection and target detection under complex backgrounds are still a major difficulty in the field of image target detection. However, fish image detection scenes often contain complex backgrounds such as water grass and reef, and fish form is small. In order to overcome the problem which is low accuracy of detection of small fish targets in complex backgrounds, In this paper, a Center Point Network 2 with Efficient Pyramid Split Attention (EPSA-CenterNet2) was proposed. The Network incorporated an Efficient Pyramid Split Attention Network (EPSANet) into CenterNet2 to improve small target detection accuracy in complex environments. In this paper, 149 images of the oplegnathus punctatus were used as a dataset to train EPSA-CenterNet2 and four other mainstream target detection networks. The experimental results showed that EPSA-CenterNet2 was superior to CenterNet2, YOLOv3, YOLOv5 and SSD in the average accuracy including AP and AP50, and the number of missed targets in small target images was less. Therefore, EPSA-Centernet2 can detect fish image targets in complex backgrounds more accurately.","PeriodicalId":348024,"journal":{"name":"Proceedings of the 5th International Conference on Control and Computer Vision","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120938468","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

Investigating YOLOv5 for Search and Rescue Operations Involving UAVs: Investigating YOLO5 调查YOLOv5用于涉及无人机的搜救行动:调查YOLO5

Proceedings of the 5th International Conference on Control and Computer Vision Pub Date : 2022-08-19 DOI: 10.1145/3561613.3561644

Namat Bachir, Q. Memon

引用次数: 4

Unsupervised Cross-domain Person Re-Identification based on Asymmetrical Pyramid Non-local Block 基于非对称金字塔非局部块的无监督跨域人物再识别

Proceedings of the 5th International Conference on Control and Computer Vision Pub Date : 2022-08-19 DOI: 10.1145/3561613.3561636

Xiangyu Li, Yuhang Zheng, Shangmin Zhou

{"title":"Unsupervised Cross-domain Person Re-Identification based on Asymmetrical Pyramid Non-local Block","authors":"Xiangyu Li, Yuhang Zheng, Shangmin Zhou","doi":"10.1145/3561613.3561636","DOIUrl":"https://doi.org/10.1145/3561613.3561636","url":null,"abstract":"The purpose of unsupervised cross-domain (UCD) person re-identification (re-ID) is to adapt the well pre-trained model on the labeled source domain to the unlabeled target domain, which tackles a more realistic problem. However, the network in the existing model cannot fully extract the features of pedestrians, so the results after clustering are not satisfactory. To address this problem, a feature extraction network model with a self-attention mechanism is proposed in this paper in order to improve the feature expression ability. We try to design and optimize the attention mechanism-based feature extraction network and similarity loss function for unsupervised person re-ID to improve the recognition accuracy. On the basis of the baseline network (such as ResNet-50), the self-attention mechanism-asymmetrical pyramid non-local block (APNB) is added to help the network learn richer global feature representation. Besides, the similarity loss function using the Euclidean distance is designed, which shows better performance than the cosine distance. Experimental results show that the proposed method has competitive performance on two public datasets Markket-1501 and DukeMTMC-Re-ID.","PeriodicalId":348024,"journal":{"name":"Proceedings of the 5th International Conference on Control and Computer Vision","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117027743","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

Three Dimensional Route Planning of UAVs based on Velocity Potential Field Method 基于速度势场法的无人机三维航路规划

Proceedings of the 5th International Conference on Control and Computer Vision Pub Date : 2022-08-19 DOI: 10.1145/3561613.3561643

Qihan Fan, Shiqiang Hu

引用次数: 0

Image-Based Storytelling Using Deep Learning 使用深度学习的基于图像的故事叙述

Proceedings of the 5th International Conference on Control and Computer Vision Pub Date : 2022-08-19 DOI: 10.1145/3561613.3561641

Yulin Zhu, Wei Yan

引用次数: 0

Research on compliant plugging strategy for electric vehicle charging port based on force feedback 基于力反馈的电动汽车充电口柔性插拔策略研究

Proceedings of the 5th International Conference on Control and Computer Vision Pub Date : 2022-08-19 DOI: 10.1145/3561613.3561648

Wenhui He, Ruifeng Li

引用次数: 0

An Adaptive Population-based Iterative Greedy Algorithm for Optimizing the Maximum Completion Time of Hybrid Flow Shop 基于自适应种群的混合流车间最大完工时间优化迭代贪心算法

Proceedings of the 5th International Conference on Control and Computer Vision Pub Date : 2022-08-19 DOI: 10.1145/3561613.3561642

Fuyou Mao, Xiyang Liu, Haomin Zhao

引用次数: 1

CBR and Focus Learning based Joint Fire Strike Plan Generation Method 基于CBR和焦点学习的联合火力打击计划生成方法

Proceedings of the 5th International Conference on Control and Computer Vision Pub Date : 2022-08-19 DOI: 10.1145/3561613.3561646

Xin Jin, Xinnian Wang, Fei Cai, Yupu Guo

引用次数: 0

DeLashNet: A Deep Network for Eyelash Artifact Removal in Ultra-Wide-Field Fundus Images DeLashNet:一种去除超宽视场眼底图像中睫毛伪影的深度网络

Proceedings of the 5th International Conference on Control and Computer Vision Pub Date : 2022-08-19 DOI: 10.1145/3561613.3561649

Dengfeng Sha, Yuhui Ma, Dan Zhang, Jiong Zhang, Yitian Zhao

{"title":"DeLashNet: A Deep Network for Eyelash Artifact Removal in Ultra-Wide-Field Fundus Images","authors":"Dengfeng Sha, Yuhui Ma, Dan Zhang, Jiong Zhang, Yitian Zhao","doi":"10.1145/3561613.3561649","DOIUrl":"https://doi.org/10.1145/3561613.3561649","url":null,"abstract":"The interference of eyelash artifacts in ultra-wide-field fundus (UWF) images has always been a serious problem in preventing precise clinical observations of pathology. Currently, the automatic removal of eyelash artifacts in UWF images remains unsolved and thus will eventually affect the diagnosis accuracy. In this paper, we propose a deep learning architecture called DeLashNet to eliminate eyelash artifacts from UWF images. Our DeLashNet consists of two stages: the first stage is the eyelash artifact removal stage based on a conditional generative adversarial network, and the second stage is the background refinement stage using an encoder-decoder structure. To solve the issue of lacking training samples with eyelashes, we design a novel eyelash growing model to generate synthetic eyelashes with labels and finally established a paired synthetic eyelashes (PSE) dataset. Experiments are conducted to verify the effectiveness of our proposed DeLashNet on eyelash artifact removal. The comparative and ablation studies demonstrate that the proposed DeLashNet achieved satisfactory removal performance on eyelash artifacts of UWF images.","PeriodicalId":348024,"journal":{"name":"Proceedings of the 5th International Conference on Control and Computer Vision","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127541299","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: 1

A Robust Image Watermarking Algorithm Based on Content Authentication and Intelligent Optimization 基于内容认证和智能优化的鲁棒图像水印算法

Proceedings of the 5th International Conference on Control and Computer Vision Pub Date : 2022-08-19 DOI: 10.1145/3561613.3561639

Yi-Lin Bei, Xiaorong Zhu, Qian Zhang, Sai Qiao

{"title":"A Robust Image Watermarking Algorithm Based on Content Authentication and Intelligent Optimization","authors":"Yi-Lin Bei, Xiaorong Zhu, Qian Zhang, Sai Qiao","doi":"10.1145/3561613.3561639","DOIUrl":"https://doi.org/10.1145/3561613.3561639","url":null,"abstract":"Aiming at some shortcomings of current digital watermarking algorithms, a color image double watermarking algorithm based on content authentication and machine learning is proposed in this paper. Firstly, the algorithm combines discrete wavelet transform (DWT) and discrete cosine transform (DCT), and embeds the watermark through singular value decomposition (SVD) modulation. Secondly, using the learning and classification characteristics of Support Vector Machine (SVM), the watermark detection model is obtained through training a large number of data, and finally the robust watermark is extracted automatically. In order to realize content authentication and tamper location, the algorithm embeds a fragile watermark at the same time, and uses the relationship between the high-frequency coefficients of wavelet transform to embed and extract the watermark. Through the simulation results and data analysis, the proposed double watermarking algorithm not only realizes the intelligent watermark extraction process, but also can accurately locate malicious tampering while maintaining strong robustness.","PeriodicalId":348024,"journal":{"name":"Proceedings of the 5th International Conference on Control and Computer Vision","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131191556","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