{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T14:40:24Z","timestamp":1777560024550,"version":"3.51.4"},"reference-count":29,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIC"],"published-print":{"date-parts":[[2023,8,21]]},"abstract":"<jats:p>The multi-layer feature pyramid structure, represented by FPN, is widely used in object detection. However, due to the aliasing effect brought by up-sampling, the current feature pyramid structure still has defects, such as loss of high-level feature information and weakening of low-level small object features. In this paper, we propose FI-FPN to solve these problems, which is mainly composed of a multi-receptive field fusion (MRF) module, contextual information filtering (CIF) module, and efficient semantic information fusion (ESF) module. Particularly, MRF stacks dilated convolutional layers and max-pooling layers to obtain receptive fields of different scales, reducing the information loss of high-level features; CIF introduces a channel attention mechanism, and the channel attention weights are reassigned; ESF introduces channel concatenation instead of element-wise operation for bottom-up feature fusion and alleviating aliasing effects, facilitating efficient information flow. Experiments show that under the ResNet50 backbone, our method improves the performance of Faster RCNN and RetinaNet by 3.5 and 4.6 mAP, respectively. Our method has competitive performance compared to other advanced methods.<\/jats:p>","DOI":"10.3233\/aic-220183","type":"journal-article","created":{"date-parts":[[2023,6,6]],"date-time":"2023-06-06T13:22:31Z","timestamp":1686057751000},"page":"191-203","source":"Crossref","is-referenced-by-count":2,"title":["FI-FPN: Feature-integration feature pyramid network for object detection"],"prefix":"10.1177","volume":"36","author":[{"given":"Qichen","family":"Su","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Chongqing University of Technology, 459 Pufu Avenue, Liangjiang New Area, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangjian","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Chongqing University of Technology, 459 Pufu Avenue, Liangjiang New Area, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuang","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Chongqing University of Technology, 459 Pufu Avenue, Liangjiang New Area, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiming","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Chongqing University of Technology, 459 Pufu Avenue, Liangjiang New Area, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/AIC-220183_ref4","doi-asserted-by":"crossref","unstructured":"Q.\u00a0Chen, Y.\u00a0Wang, T.\u00a0Yang, et al. You only look one-level feature, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp.\u00a013039\u201313048.","DOI":"10.1109\/CVPR46437.2021.01284"},{"issue":"3","key":"10.3233\/AIC-220183_ref5","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1109\/TITS.2020.2972974","article-title":"Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges","volume":"22","author":"Feng","year":"2020","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"10.3233\/AIC-220183_ref7","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The Pascal visual object classes (VOC) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"International Journal of Computer Vision"},{"key":"10.3233\/AIC-220183_ref8","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.neucom.2020.10.098","article-title":"Delving deep into the imbalance of positive proposals in two-stage object detection","volume":"425","author":"Ge","year":"2021","journal-title":"Neurocomputing"},{"key":"10.3233\/AIC-220183_ref9","doi-asserted-by":"crossref","unstructured":"G.\u00a0Ghiasi, T.Y.\u00a0Lin and Q.V.\u00a0Le, Nas-fpn: Learning scalable feature pyramid architecture for object detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp.\u00a07036\u20137045.","DOI":"10.1109\/CVPR.2019.00720"},{"key":"10.3233\/AIC-220183_ref10","doi-asserted-by":"crossref","unstructured":"R.\u00a0Girshick, J.\u00a0Donahue, T.\u00a0Darrell et al., Rich feature hierarchies for accurate object detection and semantic segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp.\u00a0580\u2013587.","DOI":"10.1109\/CVPR.2014.81"},{"key":"10.3233\/AIC-220183_ref11","doi-asserted-by":"crossref","unstructured":"Y.\u00a0Gong, X.\u00a0Yu, Y.\u00a0Ding et al., Effective fusion factor in FPN for tiny object detection, in: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2021, pp.\u00a01160\u20131168.","DOI":"10.1109\/WACV48630.2021.00120"},{"key":"10.3233\/AIC-220183_ref12","doi-asserted-by":"crossref","unstructured":"C.\u00a0Guo, B.\u00a0Fan, Q.\u00a0Zhang et al., Augfpn: Improving multi-scale feature learning for object detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp.\u00a012595\u201312604.","DOI":"10.1109\/CVPR42600.2020.01261"},{"key":"10.3233\/AIC-220183_ref13","doi-asserted-by":"crossref","unstructured":"K.\u00a0He, G.\u00a0Gkioxari, P.\u00a0Doll\u00e1r et al., Mask r-cnn, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp.\u00a02961\u20132969.","DOI":"10.1109\/ICCV.2017.322"},{"key":"10.3233\/AIC-220183_ref14","doi-asserted-by":"crossref","unstructured":"K.\u00a0He, X.\u00a0Zhang, S.\u00a0Ren et al., Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp.\u00a0770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.3233\/AIC-220183_ref15","doi-asserted-by":"crossref","unstructured":"J.\u00a0Hu, L.\u00a0Shen and G.\u00a0Sun, Squeeze-and-excitation networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp.\u00a07132\u20137141.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"10.3233\/AIC-220183_ref16","doi-asserted-by":"crossref","unstructured":"Y.\u00a0Li, Y.\u00a0Chen, N.\u00a0Wang et al., Scale-aware trident networks for object detection, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2019, pp.\u00a06054\u20136063.","DOI":"10.1109\/ICCV.2019.00615"},{"key":"10.3233\/AIC-220183_ref17","doi-asserted-by":"crossref","unstructured":"Y.\u00a0Li, Y.\u00a0Pang, J.\u00a0Shen et al., NETNet: Neighbor erasing and transferring network for better single shot object detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp.\u00a013349\u201313358.","DOI":"10.1109\/CVPR42600.2020.01336"},{"key":"10.3233\/AIC-220183_ref18","doi-asserted-by":"crossref","unstructured":"T.Y.\u00a0Lin, P.\u00a0Doll\u00e1r, R.\u00a0Girshick et al., Feature pyramid networks for object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp.\u00a02117\u20132125.","DOI":"10.1109\/CVPR.2017.106"},{"key":"10.3233\/AIC-220183_ref19","doi-asserted-by":"crossref","unstructured":"T.Y.\u00a0Lin, P.\u00a0Goyal, R.\u00a0Girshick et al., Focal loss for dense object detection, in: Proceedings of the IEEE International Conference on Computer Vision, 2017, pp.\u00a02980\u20132988.","DOI":"10.1109\/ICCV.2017.324"},{"key":"10.3233\/AIC-220183_ref20","doi-asserted-by":"crossref","unstructured":"T.Y.\u00a0Lin, M.\u00a0Maire, S.\u00a0Belongie et al., Microsoft coco: Common objects in context, in: European Conference on Computer Vision, 2014, pp.\u00a0740\u2013755.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"10.3233\/AIC-220183_ref22","doi-asserted-by":"crossref","unstructured":"S.\u00a0Liu, L.\u00a0Qi, H.\u00a0Qin et al., Path aggregation network for instance segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp.\u00a08759\u20138768.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"10.3233\/AIC-220183_ref23","doi-asserted-by":"crossref","unstructured":"W.\u00a0Liu, D.\u00a0Anguelov, D.\u00a0Erhan et al., Ssd: Single shot multibox detector, in: European Conference on Computer Vision, 2016, pp.\u00a021\u201337.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"10.3233\/AIC-220183_ref24","doi-asserted-by":"crossref","unstructured":"J.\u00a0Long, E.\u00a0Shelhamer and T.\u00a0Darrell, Fully convolutional networks for semantic segmentation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp.\u00a03431\u20133440.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"10.3233\/AIC-220183_ref26","doi-asserted-by":"crossref","unstructured":"J.\u00a0Redmon, S.\u00a0Divvala, R.\u00a0Girshick et al., You only look once: Unified, real-time object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp.\u00a0779\u2013788.","DOI":"10.1109\/CVPR.2016.91"},{"key":"10.3233\/AIC-220183_ref27","doi-asserted-by":"crossref","unstructured":"J.\u00a0Redmon and A.\u00a0Farhadi, YOLO9000: Better, faster, stronger, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp.\u00a07263\u20137271.","DOI":"10.1109\/CVPR.2017.690"},{"key":"10.3233\/AIC-220183_ref30","doi-asserted-by":"crossref","unstructured":"O.\u00a0Ronneberger, P.\u00a0Fischer and T.\u00a0Brox, U-net: Convolutional networks for biomedical image segmentation, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015, pp.\u00a0234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10.3233\/AIC-220183_ref32","doi-asserted-by":"crossref","unstructured":"M.\u00a0Tan, R.\u00a0Pang and Q.V.\u00a0Le, Efficientdet: Scalable and efficient object detection, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp.\u00a010781\u201310790.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"10.3233\/AIC-220183_ref33","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00163"},{"key":"10.3233\/AIC-220183_ref34","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.biosystemseng.2019.03.007","article-title":"Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms","volume":"181","author":"Williams","year":"2019","journal-title":"Biosystems Engineering"},{"key":"10.3233\/AIC-220183_ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2021.104341"},{"key":"10.3233\/AIC-220183_ref36","doi-asserted-by":"crossref","unstructured":"H.\u00a0Zhao, J.\u00a0Shi, X.\u00a0Qi et al., Pyramid scene parsing network, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp.\u00a02881\u20132890.","DOI":"10.1109\/CVPR.2017.660"},{"key":"10.3233\/AIC-220183_ref37","doi-asserted-by":"crossref","unstructured":"X.\u00a0Zhu, S.\u00a0Lyu, X.\u00a0Wang et al., TPH-YOLOv5: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp.\u00a02778\u20132788.","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"10.3233\/AIC-220183_ref38","unstructured":"B.\u00a0Zoph and Q.V.\u00a0Le, Neural architecture search with reinforcement learning, in: International Conference on Learning Representations, 2018, pp.\u00a08697\u20138710."}],"container-title":["AI Communications"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/AIC-220183","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T18:28:08Z","timestamp":1777400888000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/AIC-220183"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,21]]},"references-count":29,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/aic-220183","relation":{},"ISSN":["1875-8452","0921-7126"],"issn-type":[{"value":"1875-8452","type":"electronic"},{"value":"0921-7126","type":"print"}],"subject":[],"published":{"date-parts":[[2023,8,21]]}}}