{"title":"SODSR:一种基于优化组合的三阶段超分辨率小目标检测方法","authors":"Xiaoyong Mei;Kejin Zhang;Changqin Huang;Xiao Chen;Ming Li;Zhao Li;Weiping Ding;Xindong Wu","doi":"10.1109/TETCI.2024.3452749","DOIUrl":null,"url":null,"abstract":"Face detection is a fundamental task in computer vision, yet remains challenging in educational settings due to the presence of objects of various sizes. Subpar detection can significantly impede subsequent tasks' performance. To address this, we present a novel framework, Small Object Detection Super Resolution (SODSR), inspired by super resolution (SR) techniques for feature-level images. SODSR comprises three stages: (1) Constructing a 3D model evaluation matrix to select optimal model combinations based on detection accuracy and image quality metrics. (2) Employing Double-thread FDN in the first stage to preprocess images, enhancing feature resolution for potential facial objects. (3) Leveraging Multi-head HyperNet in the second stage to augment face feature detection and improve accuracy. Finally, in the third stage, we introduce AFPGAN, a facial prior feature enhancement network, coupled with StyleGAN2 for texture and contour detail enhancement. Experimental results demonstrate that SODSR outperforms existing small object detection (SOD) models in both accuracy and visual fidelity.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2410-2426"},"PeriodicalIF":5.3000,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SODSR: A Three-Stage Small Object Detection via Super-Resolution Using Optimizing Combination\",\"authors\":\"Xiaoyong Mei;Kejin Zhang;Changqin Huang;Xiao Chen;Ming Li;Zhao Li;Weiping Ding;Xindong Wu\",\"doi\":\"10.1109/TETCI.2024.3452749\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Face detection is a fundamental task in computer vision, yet remains challenging in educational settings due to the presence of objects of various sizes. Subpar detection can significantly impede subsequent tasks' performance. To address this, we present a novel framework, Small Object Detection Super Resolution (SODSR), inspired by super resolution (SR) techniques for feature-level images. SODSR comprises three stages: (1) Constructing a 3D model evaluation matrix to select optimal model combinations based on detection accuracy and image quality metrics. (2) Employing Double-thread FDN in the first stage to preprocess images, enhancing feature resolution for potential facial objects. (3) Leveraging Multi-head HyperNet in the second stage to augment face feature detection and improve accuracy. Finally, in the third stage, we introduce AFPGAN, a facial prior feature enhancement network, coupled with StyleGAN2 for texture and contour detail enhancement. Experimental results demonstrate that SODSR outperforms existing small object detection (SOD) models in both accuracy and visual fidelity.\",\"PeriodicalId\":13135,\"journal\":{\"name\":\"IEEE Transactions on Emerging Topics in Computational Intelligence\",\"volume\":\"9 3\",\"pages\":\"2410-2426\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Emerging Topics in Computational Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10684780/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Emerging Topics in Computational Intelligence","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10684780/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
SODSR: A Three-Stage Small Object Detection via Super-Resolution Using Optimizing Combination
Face detection is a fundamental task in computer vision, yet remains challenging in educational settings due to the presence of objects of various sizes. Subpar detection can significantly impede subsequent tasks' performance. To address this, we present a novel framework, Small Object Detection Super Resolution (SODSR), inspired by super resolution (SR) techniques for feature-level images. SODSR comprises three stages: (1) Constructing a 3D model evaluation matrix to select optimal model combinations based on detection accuracy and image quality metrics. (2) Employing Double-thread FDN in the first stage to preprocess images, enhancing feature resolution for potential facial objects. (3) Leveraging Multi-head HyperNet in the second stage to augment face feature detection and improve accuracy. Finally, in the third stage, we introduce AFPGAN, a facial prior feature enhancement network, coupled with StyleGAN2 for texture and contour detail enhancement. Experimental results demonstrate that SODSR outperforms existing small object detection (SOD) models in both accuracy and visual fidelity.
期刊介绍:
The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys.
TETCI is an electronics only publication. TETCI publishes six issues per year.
Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.