{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T20:57:13Z","timestamp":1771880233342,"version":"3.50.1"},"reference-count":41,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neural Networks"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.neunet.2025.108414","type":"journal-article","created":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T16:59:48Z","timestamp":1766077188000},"page":"108414","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["BDM-YOLOv8n: A high-performance model for accurate fire detection in aerial imagery"],"prefix":"10.1016","volume":"197","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2664-1829","authenticated-orcid":false,"given":"Laohu","family":"Yuan","sequence":"first","affiliation":[]},{"given":"Peng","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Zhiyuan","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2465-567X","authenticated-orcid":false,"given":"Jiafu","family":"Liu","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neunet.2025.108414_bib0001","doi-asserted-by":"crossref","unstructured":"An, Z., Sun, G., Liu, Y., Li, R., Wu, M., Cheng, M.-M., Konukoglu, E., Belongie, S. (2024). Multimodality helps few-shot 3D point cloud semantic segmentation. arXiv: 2410.22489.","DOI":"10.1109\/CVPR52733.2024.00383"},{"key":"10.1016\/j.neunet.2025.108414_bib0002","unstructured":"An, Z., Sun, G., Wu, Z., Tang, H., Van Gool, L. (2023). Temporal-aware hierarchical mask classification for video semantic segmentation. arXiv: 2309.08020."},{"key":"10.1016\/j.neunet.2025.108414_bib0003","article-title":"Sensor-based and vision-based human activity recognition: A comprehensive survey","volume":"108","author":"Dang","year":"2020","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neunet.2025.108414_bib0004","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"10886","article-title":"Diverse branch block: Building a convolution as an inception-like unit","author":"Ding","year":"2021"},{"key":"10.1016\/j.neunet.2025.108414_bib0005","series-title":"International conference on pattern recognition and artificial intelligence","first-page":"40","article-title":"Comparative study of activation functions and their impact on the YOLOv5 object detection model","author":"Doherty","year":"2022"},{"key":"10.1016\/j.neunet.2025.108414_bib0006","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015). Fast R-CNN. arXiv: 1504.08083.","DOI":"10.1109\/ICCV.2015.169"},{"key":"10.1016\/j.neunet.2025.108414_bib0007","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"580","article-title":"Rich feature hierarchies for accurate object detection and semantic segmentation","author":"Girshick","year":"2014"},{"key":"10.1016\/j.neunet.2025.108414_bib0008","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2025.117019","article-title":"SA-FPN: Scale-aware attention-guided feature pyramid network for small object detection on surface defect detection of steel strips","volume":"249","author":"Han","year":"2025","journal-title":"Measurement"},{"key":"10.1016\/j.neunet.2025.108414_bib0009","doi-asserted-by":"crossref","first-page":"65254","DOI":"10.1109\/ACCESS.2024.3385856","article-title":"DCGC-YOLO: The efficient dual-channel bottleneck structure yolo detection algorithm for fire detection","volume":"12","author":"He","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.neunet.2025.108414_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111425","article-title":"FSENet: Feature suppression and enhancement network for tiny object detection","volume":"162","author":"Hu","year":"2025","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neunet.2025.108414_bib0011","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.110041","article-title":"Discriminative features enhancement for low-altitude UAV object detection","volume":"147","author":"Huang","year":"2024","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neunet.2025.108414_bib0012","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.110041","article-title":"Discriminative features enhancement for low-altitude UAV object detection","volume":"147","author":"Huang","year":"2024","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neunet.2025.108414_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2022.103812","article-title":"A comprehensive review of object detection with deep learning","volume":"132","author":"Kaur","year":"2023","journal-title":"Digital Signal Processing"},{"key":"10.1016\/j.neunet.2025.108414_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.105403","article-title":"Randomly initialized CNN with densely connected stacked autoencoder for efficient fire detection","volume":"116","author":"Khan","year":"2022","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.neunet.2025.108414_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.111273","article-title":"Optimized cross-module attention network and medium-scale dataset for effective fire detection","volume":"161","author":"Khan","year":"2025","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neunet.2025.108414_bib0016","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"6990","article-title":"Equalized focal loss for dense long-tailed object detection","author":"Li","year":"2022"},{"issue":"8","key":"10.1016\/j.neunet.2025.108414_bib0017","doi-asserted-by":"crossref","first-page":"6662","DOI":"10.1109\/TGRS.2020.3029945","article-title":"ComNet: Combinational neural network for object detection in UAV-borne thermal images","volume":"59","author":"Li","year":"2020","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"10.1016\/j.neunet.2025.108414_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.jvcir.2021.103058","article-title":"A lightweight multi-scale aggregated model for detecting aerial images captured by UAVs","volume":"77","author":"Li","year":"2021","journal-title":"Journal of Visual Communication and Image Representation"},{"key":"10.1016\/j.neunet.2025.108414_bib0019","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.neucom.2020.06.011","article-title":"Novel up-scale feature aggregation for object detection in aerial images","volume":"411","author":"Lin","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neunet.2025.108414_bib0020","series-title":"Computer vision\u2013ECCV 2016: 14th european conference, amsterdam, the netherlands, october 11\u201314, 2016, proceedings, part i 14","first-page":"21","article-title":"SSD: Single shot multibox detector","author":"Liu","year":"2016"},{"key":"10.1016\/j.neunet.2025.108414_bib0021","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.111298","article-title":"MFECNet: Multi-level feature enhancement and correspondence network for few-shot anomaly detection of high-speed trains","volume":"161","author":"Liu","year":"2025","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neunet.2025.108414_bib0022","unstructured":"Ma, S., Xu, Y. (2023). MPDIoU: A loss for efficient and accurate bounding box regression. arXiv: 2307.07662."},{"issue":"11","key":"10.1016\/j.neunet.2025.108414_bib0023","doi-asserted-by":"crossref","first-page":"2171","DOI":"10.3390\/rs13112171","article-title":"Improved yolo network for free-angle remote sensing target detection","volume":"13","author":"Qing","year":"2021","journal-title":"Remote Sensing"},{"key":"10.1016\/j.neunet.2025.108414_bib0024","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","article-title":"You only look once: Unified, real-time object detection","author":"Redmon","year":"2016"},{"issue":"6","key":"10.1016\/j.neunet.2025.108414_bib0025","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2016","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.neunet.2025.108414_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.107997","article-title":"Scale-balanced loss for object detection","volume":"117","author":"Shuang","year":"2021","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neunet.2025.108414_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109761","article-title":"Learning discriminative feature representation with pixel-level supervision for forest smoke recognition","volume":"143","author":"Tao","year":"2023","journal-title":"Pattern Recognition"},{"issue":"4","key":"10.1016\/j.neunet.2025.108414_bib0028","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas","volume":"5","author":"Terven","year":"2023","journal-title":"Machine Learning and Knowledge Extraction"},{"issue":"6","key":"10.1016\/j.neunet.2025.108414_bib0029","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1007\/s11633-023-1445-5","article-title":"Hybrid CBAM-efficientnetv2 fire image recognition method with label smoothing in detecting tiny targets","volume":"21","author":"Wang","year":"2024","journal-title":"Machine Intelligence Research"},{"key":"10.1016\/j.neunet.2025.108414_bib0030","series-title":"European conference on computer vision","first-page":"1","article-title":"Yolov9: Learning what you want to learn using programmable gradient information","author":"Wang","year":"2025"},{"key":"10.1016\/j.neunet.2025.108414_bib0031","series-title":"International conference on machine learning","first-page":"11863","article-title":"SimAM: A simple, parameter-free attention module for convolutional neural networks","author":"Yang","year":"2021"},{"key":"10.1016\/j.neunet.2025.108414_bib0032","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109722","article-title":"Preferred vector machine for forest fire detection","volume":"143","author":"Yang","year":"2023","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neunet.2025.108414_bib0033","doi-asserted-by":"crossref","first-page":"6331","DOI":"10.1109\/TIP.2022.3207006","article-title":"Optimized dual fire attention network and medium-scale fire classification benchmark","volume":"31","author":"Yar","year":"2022","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.neunet.2025.108414_bib0034","doi-asserted-by":"crossref","first-page":"6331","DOI":"10.1109\/TIP.2022.3207006","article-title":"Optimized dual fire attention network and medium-scale fire classification benchmark","volume":"31","author":"Yar","year":"2022","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.neunet.2025.108414_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.111119","article-title":"A newton interpolation network for smoke semantic segmentation","volume":"159","author":"Yuan","year":"2025","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neunet.2025.108414_bib0036","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.111119","article-title":"A newton interpolation network for smoke semantic segmentation","volume":"159","author":"Yuan","year":"2025","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neunet.2025.108414_bib0037","unstructured":"Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., Ni, L. M., Shum, H.-Y. (2022). DINO: DETR with improved denoising anchor boxes for end-to-end object detection. arXiv: 2203.03605."},{"key":"10.1016\/j.neunet.2025.108414_bib0038","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2022.105628","article-title":"Development of a cross-scale weighted feature fusion network for hot-rolled steel surface defect detection","volume":"117","author":"Zhang","year":"2023","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.neunet.2025.108414_bib0039","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111415","article-title":"Inversed pyramid network with spatial-adapted and task-oriented tuning for few-shot learning","volume":"164","author":"Zhao","year":"2025","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neunet.2025.108414_bib0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111415","article-title":"Inversed pyramid network with spatial-adapted and task-oriented tuning for few-shot learning","volume":"164","author":"Zhao","year":"2025","journal-title":"Pattern Recognition"},{"issue":"11","key":"10.1016\/j.neunet.2025.108414_bib0041","doi-asserted-by":"crossref","first-page":"12556","DOI":"10.1007\/s10489-021-03121-8","article-title":"SA-FPN: An effective feature pyramid network for crowded human detection","volume":"52","author":"Zhou","year":"2022","journal-title":"Applied Intelligence"}],"container-title":["Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S089360802501295X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S089360802501295X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T19:58:42Z","timestamp":1771876722000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S089360802501295X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":41,"alternative-id":["S089360802501295X"],"URL":"https:\/\/doi.org\/10.1016\/j.neunet.2025.108414","relation":{},"ISSN":["0893-6080"],"issn-type":[{"value":"0893-6080","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"BDM-YOLOv8n: A high-performance model for accurate fire detection in aerial imagery","name":"articletitle","label":"Article Title"},{"value":"Neural Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neunet.2025.108414","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"108414"}}