{"_buckets": {"deposit": "9d5c004e-ad46-45a0-883e-1e01f341ea42"}, "_deposit": {"created_by": 15, "id": "2000405", "owner": "15", "owners": [15], "owners_ext": {"displayname": "北見工業大学学術機関リポジトリ（KIT-R）", "username": "kitir"}, "pid": {"revision_id": 0, "type": "depid", "value": "2000405"}, "status": "published"}, "_oai": {"id": "oai:kitami-it.repo.nii.ac.jp:02000405", "sets": []}, "author_link": [], "control_number": "2000405", "item_7_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2023-03", "bibliographicIssueDateType": "Issued"}}]}, "item_7_date_granted_63": {"attribute_name": "学位授与年月日", "attribute_value_mlt": [{"subitem_dategranted": "2023-03-17"}]}, "item_7_degree_grantor_61": {"attribute_name": "学位授与機関", "attribute_value_mlt": [{"subitem_degreegrantor": [{"subitem_degreegrantor_language": "ja", "subitem_degreegrantor_name": "北見工業大学"}], "subitem_degreegrantor_identifier": [{"subitem_degreegrantor_identifier_name": "10106", "subitem_degreegrantor_identifier_scheme": "kakenhi"}]}]}, "item_7_degree_name_60": {"attribute_name": "学位名", "attribute_value_mlt": [{"subitem_degreename": "博士（工学）", "subitem_degreename_language": "ja"}]}, "item_7_description_4": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "Recently, object detection based on deep convolutional neural networks (CNNs) have achieved \nremarkable result and successfully applied many real-world applications. However, scale variation \nproblem in multiscale object detection still is challenging problem, especially for small objects. \nConcerning the above problem, we proposed a new detection network with an efficient feature fusion \nmodule based on SSD using VGG-16 as backbone called Multi-path Feature Fusion Single Shot Multibox Detector (MF-SSD). The proposed feature fusion module consists of two newly designed modules \nwith dilated convolution, which fuses features from shallow layers (mainly contain boundary information) \nto highRecently, object detection based on deep convolutional neural networks (CNNs) have achieved \nremarkable result and successfully applied many real-world applications. However, scale variation \nproblem in multiscale object detection still is challenging problem, especially for small objects. \nConcerning the above problem, we proposed a new detection network with an efficient feature fusion \nmodule based on SSD using VGG-16 as backbone called Multi-path Feature Fusion Single Shot Multibox Detector (MF-SSD). The proposed feature fusion module consists of two newly designed modules \nwith dilated convolution, which fuses features from shallow layers (mainly contain boundary information) \nto higher level features (mainly contain semantic reach information) without reducing the original \nresolution of the feature map. We have conducted experiments on three datasets to explicate the efficacy \nof our proposed detector. The proposed MF-SSD with input size 512×512 achieved 81.5% mAP and \n34.1 % mAP on PASCAL VOC test set and MS COCO test-dev, respectively. Experimental results show \nthe proposed feature fusion module can improve both semantic and boundary information for object \ndetectioner level features (mainly contain semantic reach information) without reducing the original \nresolution of the feature map. We have conducted experiments on three datasets to explicate the efficacy \nof our proposed detector. The proposed MF-SSD with input size 512×512 achieved 81.5% mAP and \n34.1 % mAP on PASCAL VOC test set and MS COCO test-dev, respectively. Experimental results show \nthe proposed feature fusion module can improve both semantic and boundary information for object detection.", "subitem_description_language": "en", "subitem_description_type": "Abstract"}]}, "item_7_dissertation_number_64": {"attribute_name": "学位授与番号", "attribute_value_mlt": [{"subitem_dissertationnumber": "甲第208号"}]}, "item_7_identifier_registration": {"attribute_name": "ID登録", "attribute_value_mlt": [{"subitem_identifier_reg_text": "10.19000/0002000405", "subitem_identifier_reg_type": "JaLC"}]}, "item_7_publisher_32": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "北見工業大学", "subitem_publisher_language": "ja"}]}, "item_7_select_15": {"attribute_name": "著者版フラグ", "attribute_value_mlt": [{"subitem_select_item": "ETD", "subitem_select_language": "en"}]}, "item_7_text_65": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_text_language": "en", "subitem_text_value": "https://kitami-it.repo.nii.ac.jp/records/2000405"}]}, "item_7_text_66": {"attribute_name": "研究科・専攻名", "attribute_value_mlt": [{"subitem_text_language": "ja", "subitem_text_value": "生産基盤工学専攻"}]}, "item_access_right": {"attribute_name": "アクセス権", "attribute_value_mlt": [{"subitem_access_right": "open access", "subitem_access_right_uri": "http://purl.org/coar/access_right/c_abf2"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "TUERSUNJIANG YIMAMU", "creatorNameLang": "en"}]}, {"creatorNames": [{"creatorName": "トルスンジャン・イマム", "creatorNameLang": "ja"}]}]}, "item_files": {"attribute_name": "ファイル情報", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2023-04-03"}], "download_preview_message": "", "file_order": 0, "filename": "Doctoral Thesis_TUERSUNJIANG YIMAMU-1 .pdf", "filesize": [{"value": "1.9 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "mimetype": "application/pdf", "size": 1900000.0, "url": {"objectType": "fulltext", "url": "https://kitami-it.repo.nii.ac.jp/record/2000405/files/Doctoral Thesis_TUERSUNJIANG YIMAMU-1 .pdf"}, "version_id": "25365aac-67b4-4b82-912c-bb3532cb88a9"}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"resourcetype": "doctoral thesis", "resourceuri": "http://purl.org/coar/resource_type/c_db06"}]}, "item_title": "Feature Extraction for Single Shot Multibox Object Detector", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "Feature Extraction for Single Shot Multibox Object Detector", "subitem_title_language": "en"}, {"subitem_title": "単画像多矩形物体検出のための特徴抽出", "subitem_title_language": "ja"}]}, "item_type_id": "7", "owner": "15", "path": ["6"], "permalink_uri": "https://doi.org/10.19000/0002000405", "pubdate": {"attribute_name": "PubDate", "attribute_value": "2023-04-03"}, "publish_date": "2023-04-03", "publish_status": "0", "recid": "2000405", "relation": {}, "relation_version_is_last": true, "title": ["Feature Extraction for Single Shot Multibox Object Detector"], "weko_shared_id": -1}