{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105230"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105230","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Multiple scale sharing faster-RCNN","abstract":"The student, Siwei Tang, submitted this Thesis for approval on 2019-04-24 at 10:10.","abstract_html":"The student, Siwei Tang, submitted this Thesis for approval on 2019-04-24 at 10:10.","abstract_has_math":false,"creators":["Tang, Siwei"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Shi, Honghui"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-23T20:48:20Z","date_published":"2019-08-23T20:48:20Z","updated_at":"2026-07-22T22:24:44Z","subjects":["deep-learning","Faster-RCNN","small object detection"],"languages":["en"],"rights":["Copyright 2019 Siwei Tang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105230","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Shi, Honghui"]},{"key":"dc:creator","label":"Author","values":["Tang, Siwei"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T20:48:20Z","2021-08-24T09:15:10Z","2019-04-24","2019-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["deep-learning","Faster-RCNN","small object detection"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Siwei Tang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105230"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The student, Siwei Tang, submitted this Thesis for approval on 2019-04-24 at 10:10.","This Thesis was approved for publication on 2019-04-24 at 13:17.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13771 on 2019-08-22 at 16:23:20","Made available in DSpace on 2019-08-23T20:48:20Z (GMT). No. of bitstreams: 2 TANG-THESIS-2019.pdf: 681796 bytes, checksum: 2c015d2dfaaa358501f9c6f1ab80b395 (MD5) LICENSE.txt: 4207 bytes, checksum: 59f71a91dfa4f8be3bed256450de5c74 (MD5) Previous issue date: 2019-04-24","Embargo set by: Seth Robbins for item 112352 Lift date: 2021-08-23T20:48:32Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 112352 on 2021-08-24T09:15:10Z.","Small object detection is a challenging task in the field of computer vision because the objects are always of low resolution in the original image and can be easily affected by noise. The state-of-the-art Faster RCNN object detector has good capacity of detecting large objects while small object detection is not one of its advantages. This thesis presents a novel object detector Multi-Scale Sharing Faster-RCNN (MSS-FRCNN) to solve the problem of poor detection performance of small objects by Faster RCNN. We find that upsampling the input image can benefit the small object detection performance. So MSS-FRCNN takes two images with different scales as input and then uses the two feature maps extracted from two images for RoI generation independently. Finally, the model merges the two feature map for classification and bounding box regression. We test our model with two datasets Tsinghua-Tencent 100k and Pascal VOC 07+12. The result demonstrates that MSS-FRCNN can outperform original Faster RCNN in small object detection.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-05-01","The student, Siwei Tang, accepted the attached license on 2019-04-24 at 09:11."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Multiple scale sharing faster-RCNN"]}]}],"canonical_facts":{"dc:contributor":["Shi, Honghui"],"dc:creator":["Tang, Siwei"],"dc:date":["2019-08-23T20:48:20Z","2021-08-24T09:15:10Z","2019-04-24","2019-05"],"dc:description":["The student, Siwei Tang, submitted this Thesis for approval on 2019-04-24 at 10:10.","This Thesis was approved for publication on 2019-04-24 at 13:17.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13771 on 2019-08-22 at 16:23:20","Made available in DSpace on 2019-08-23T20:48:20Z (GMT). No. of bitstreams: 2 TANG-THESIS-2019.pdf: 681796 bytes, checksum: 2c015d2dfaaa358501f9c6f1ab80b395 (MD5) LICENSE.txt: 4207 bytes, checksum: 59f71a91dfa4f8be3bed256450de5c74 (MD5) Previous issue date: 2019-04-24","Embargo set by: Seth Robbins for item 112352 Lift date: 2021-08-23T20:48:32Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 112352 on 2021-08-24T09:15:10Z.","Small object detection is a challenging task in the field of computer vision because the objects are always of low resolution in the original image and can be easily affected by noise. The state-of-the-art Faster RCNN object detector has good capacity of detecting large objects while small object detection is not one of its advantages. This thesis presents a novel object detector Multi-Scale Sharing Faster-RCNN (MSS-FRCNN) to solve the problem of poor detection performance of small objects by Faster RCNN. We find that upsampling the input image can benefit the small object detection performance. So MSS-FRCNN takes two images with different scales as input and then uses the two feature maps extracted from two images for RoI generation independently. Finally, the model merges the two feature map for classification and bounding box regression. We test our model with two datasets Tsinghua-Tencent 100k and Pascal VOC 07+12. The result demonstrates that MSS-FRCNN can outperform original Faster RCNN in small object detection.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2021-05-01","The student, Siwei Tang, accepted the attached license on 2019-04-24 at 09:11."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/105230"],"dc:language":["en"],"dc:rights":["Copyright 2019 Siwei Tang"],"dc:subject":["deep-learning","Faster-RCNN","small object detection"],"dc:title":["Multiple scale sharing faster-RCNN"],"dc:type":["text"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:44Z"}