{"title":"基于FPN的并行特征融合模块PFF-FPN在行人检测中的应用","authors":"Guiyi Yang, Zhengyou Wang, Shanna Zhuang","doi":"10.1109/ICCEAI52939.2021.00075","DOIUrl":null,"url":null,"abstract":"Feature extraction in pedestrian detection is a challenging problem due to the different sizes of pedestrians and occlusion in pedestrians. Currently, Feature Pypyramid Networks(FPN) structure is usually used in pedestrian detection networks for feature extraction but aiming at the characteristics of pedestrian detection tasks it may not be effective in extracting important layer feature information. Therefore, this paper proposes a module based on PFN structure with parallel feature fusion named PFF-FPN. PFF-FPN uses three different FPNs to extract feature and fuses the corresponding layer feature to reinforce the focused layer feature information. In pedestrian detection task PFF-FPN can be adapted to different network frameworks and it also gets a good performance.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"PFF-FPN: A Parallel Feature Fusion Module Based on FPN in Pedestrian Detection\",\"authors\":\"Guiyi Yang, Zhengyou Wang, Shanna Zhuang\",\"doi\":\"10.1109/ICCEAI52939.2021.00075\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Feature extraction in pedestrian detection is a challenging problem due to the different sizes of pedestrians and occlusion in pedestrians. Currently, Feature Pypyramid Networks(FPN) structure is usually used in pedestrian detection networks for feature extraction but aiming at the characteristics of pedestrian detection tasks it may not be effective in extracting important layer feature information. Therefore, this paper proposes a module based on PFN structure with parallel feature fusion named PFF-FPN. PFF-FPN uses three different FPNs to extract feature and fuses the corresponding layer feature to reinforce the focused layer feature information. In pedestrian detection task PFF-FPN can be adapted to different network frameworks and it also gets a good performance.\",\"PeriodicalId\":331409,\"journal\":{\"name\":\"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCEAI52939.2021.00075\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCEAI52939.2021.00075","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
PFF-FPN: A Parallel Feature Fusion Module Based on FPN in Pedestrian Detection
Feature extraction in pedestrian detection is a challenging problem due to the different sizes of pedestrians and occlusion in pedestrians. Currently, Feature Pypyramid Networks(FPN) structure is usually used in pedestrian detection networks for feature extraction but aiming at the characteristics of pedestrian detection tasks it may not be effective in extracting important layer feature information. Therefore, this paper proposes a module based on PFN structure with parallel feature fusion named PFF-FPN. PFF-FPN uses three different FPNs to extract feature and fuses the corresponding layer feature to reinforce the focused layer feature information. In pedestrian detection task PFF-FPN can be adapted to different network frameworks and it also gets a good performance.