{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T17:43:15Z","timestamp":1792690995208,"version":"4.5.0"},"reference-count":42,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T00:00:00Z","timestamp":1792627200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Infrared object detection plays an important role in low-light and complex-environment perception. However, infrared images often exhibit low signal-to-noise ratio, weak target-background contrast, and limited texture information, which can degrade detection performance in small-target and cluttered-background scenarios while making it difficult to balance detection accuracy and model complexity. To address these challenges, this paper proposes TCLNet, a Thermal Contrast-Aware Lightweight Network for infrared object detection. TCLNet introduces a Thermal Contrast Encoder (TCE) to enhance local contrast-sensitive target representations and a Thermal-Guided Cross-Feature Attention (TGCA) mechanism to promote interactions between contrast-enhanced and semantic features, thereby improving target discrimination and suppressing background interference.Experiments were conducted independently on the M3FD, FLIR ADAS v2 (denoted as FLIR_2), and NUDT-SIRST1 datasets. TCLNet achieves mAP50 values of 60.5%, 45.4%, and 91.4%, respectively, corresponding to improvements of 12.8, 11.3, and 7.5 percentage points over the YOLO12n baseline. Meanwhile, the model maintains a compact size of approximately 2M parameters. These results demonstrate that TCLNet improves infrared target detection, particularly in low-SNR and small-target conditions, while achieving a favorable balance between detection accuracy and model complexity.<\/jats:p>","DOI":"10.1371\/journal.pone.0361114","type":"journal-article","created":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T17:26:25Z","timestamp":1792689985000},"page":"e0361114","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["TCLNet: A Thermal Contrast-Aware Lightweight Network for infrared object detection"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-1723-9284","authenticated-orcid":true,"given":"Xiyuan","family":"Zhu","sequence":"first","affiliation":[{"name":"Xijing University","place":["Xi\u2019an, Shaanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongtao","family":"Dang","sequence":"additional","affiliation":[{"name":"Xijing University","place":["Xi\u2019an, Shaanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyuan","family":"Jin","sequence":"additional","affiliation":[{"name":"Xijing University","place":["Xi\u2019an, Shaanxi, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,10,22]]},"reference":[{"issue":"1","key":"pone.0361114.ref001","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/s00138-013-0570-5","article-title":"Thermal cameras and applications: a survey","volume":"25","author":"R Gade","year":"2013","journal-title":"Machine Vision and Applications"},{"issue":"7","key":"pone.0361114.ref002","doi-asserted-by":"crossref","first-page":"12305","DOI":"10.3390\/s140712305","article-title":"Infrared thermography for temperature measurement and non-destructive testing","volume":"14","author":"R Usamentiaga","year":"2014","journal-title":"Sensors (Basel)"},{"issue":"11","key":"pone.0361114.ref003","doi-asserted-by":"crossref","first-page":"9813","DOI":"10.1109\/TGRS.2020.3044958","article-title":"Attentional Local Contrast Networks for Infrared Small Target Detection","volume":"59","author":"Y Dai","year":"2021","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"pone.0361114.ref004","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/S0079-6727(02)00024-1","article-title":"Infrared detectors: status and trends","volume":"27","author":"A Rogalski","year":"2003","journal-title":"Progress in Quantum Electronics"},{"key":"pone.0361114.ref005","doi-asserted-by":"crossref","first-page":"109788","DOI":"10.1016\/j.patcog.2023.109788","article-title":"Infrared small target segmentation networks: A survey","volume":"143","author":"R Kou","year":"2023","journal-title":"Pattern Recognition"},{"key":"pone.0361114.ref006","doi-asserted-by":"crossref","first-page":"170134","DOI":"10.1016\/j.ijleo.2022.170134","article-title":"Multiscale contrast enhancement method for small infrared target detection","volume":"271","author":"S Zhong","year":"2022","journal-title":"Optik"},{"key":"pone.0361114.ref007","doi-asserted-by":"crossref","unstructured":"Girshick R, Donahue J, Darrell T. Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014. 580\u20137.","DOI":"10.1109\/CVPR.2014.81"},{"key":"pone.0361114.ref008","doi-asserted-by":"crossref","unstructured":"Girshick R. Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, 2015. 1440\u20138.","DOI":"10.1109\/ICCV.2015.169"},{"key":"pone.0361114.ref009","article-title":"Faster R-CNN: Towards real-time object detection with region. arXiv preprint arXiv:1506.01497","author":"S Ren","year":"2015"},{"issue":"6","key":"pone.0361114.ref010","first-page":"1137","article-title":"Proposal networks","volume":"39","year":"2016","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"pone.0361114.ref011","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P. Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, 2017. 2961\u20139.","DOI":"10.1109\/ICCV.2017.322"},{"key":"pone.0361114.ref012","doi-asserted-by":"crossref","unstructured":"Cai Z, Vasconcelos N. Cascade r-cnn: Delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018. 6154\u201362.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"pone.0361114.ref013","doi-asserted-by":"crossref","unstructured":"Cao J, Cholakkal H, Anwer RM. D2det: Towards high quality object detection and instance segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 2020. 11485\u201394.","DOI":"10.1109\/CVPR42600.2020.01150"},{"key":"pone.0361114.ref014","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, et al. Ssd: Single shot multibox detector. In: Computer Vision\u2013 ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I, 2016. 21\u201337.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"pone.0361114.ref015","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R. You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016. 779\u201388.","DOI":"10.1109\/CVPR.2016.91"},{"key":"pone.0361114.ref016","doi-asserted-by":"crossref","unstructured":"Varghese R, Sambath M. Yolov8: A novel object detection algorithm with enhanced performance and robustness. In: 2024. 1\u20136.","DOI":"10.1109\/ADICS58448.2024.10533619"},{"key":"pone.0361114.ref017","article-title":"Yolov11: An overview of the key architectural enhancements","author":"R Khanam","year":"2024","journal-title":"arXiv preprint"},{"key":"pone.0361114.ref018","article-title":"YOLO12: Attention-centric real-time object detectors","author":"Y Tian","year":"2025","journal-title":"arXiv preprint"},{"key":"pone.0361114.ref019","doi-asserted-by":"crossref","unstructured":"Carion N, Massa F, Synnaeve G. End-to-end object detection with transformers. In: European conference on computer vision, 2020. 213\u201329.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"pone.0361114.ref020","doi-asserted-by":"crossref","unstructured":"Meng D, Chen X, Fan Z, Zeng G, Li H, Yuan Y, et al. Conditional DETR for Fast Training Convergence. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), 2021. 3631\u201340. https:\/\/doi.org\/10.1109\/iccv48922.2021.00363","DOI":"10.1109\/ICCV48922.2021.00363"},{"key":"pone.0361114.ref021","doi-asserted-by":"crossref","unstructured":"Zhao Y, Lv W, Xu S. Detrs beat yolos on real-time object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 2024. 16965\u201374.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"pone.0361114.ref022","article-title":"DEIM: DETR with Improved Matching for Fast Convergence","author":"S Huang","year":"2024","journal-title":"arXiv preprint"},{"key":"pone.0361114.ref023","unstructured":"Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2001. I\u2013I."},{"key":"pone.0361114.ref024","doi-asserted-by":"crossref","unstructured":"Dalal N, Triggs B. Histograms of Oriented Gradients for Human Detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905). 886\u201393. https:\/\/doi.org\/10.1109\/cvpr.2005.177","DOI":"10.1109\/CVPR.2005.177"},{"issue":"2","key":"pone.0361114.ref025","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"DG Lowe","year":"2004","journal-title":"International Journal of Computer Vision"},{"issue":"12","key":"pone.0361114.ref026","doi-asserted-by":"crossref","first-page":"4996","DOI":"10.1109\/TIP.2013.2281420","article-title":"Infrared patch-image model for small target detection in a single image","volume":"22","author":"C Gao","year":"2013","journal-title":"IEEE Trans Image Process"},{"issue":"1","key":"pone.0361114.ref027","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/TGRS.2013.2242477","article-title":"A Local Contrast Method for Small Infrared Target Detection","volume":"52","author":"CLP Chen","year":"2014","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"pone.0361114.ref028","doi-asserted-by":"crossref","unstructured":"Chenlei Guo, Qi Ma, Liming Zhang. Spatio-temporal Saliency detection using phase spectrum of quaternion fourier transform. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008. 1\u20138. https:\/\/doi.org\/10.1109\/cvpr.2008.4587715","DOI":"10.1109\/CVPR.2008.4587715"},{"key":"pone.0361114.ref029","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.infrared.2015.01.017","article-title":"Adaptive detection method of infrared small target based on target-background separation via robust principal component analysis","volume":"69","author":"C Wang","year":"2015","journal-title":"Infrared Physics & Technology"},{"issue":"1","key":"pone.0361114.ref030","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1109\/TIP.2009.2030969","article-title":"A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression","volume":"19","author":"C Guo","year":"2010","journal-title":"IEEE Trans Image Process"},{"key":"pone.0361114.ref031","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2023.3243062","article-title":"One-Stage Cascade Refinement Networks for Infrared Small Target Detection","volume":"61","author":"Y Dai","year":"2023","journal-title":"IEEE Trans Geosci Remote Sensing"},{"issue":"6","key":"pone.0361114.ref032","doi-asserted-by":"crossref","first-page":"933","DOI":"10.3390\/electronics11060933","article-title":"Infrared Image Small-Target Detection Based on Improved FCOS and Spatio-Temporal Features","volume":"11","author":"S Yao","year":"2022","journal-title":"Electronics"},{"key":"pone.0361114.ref033","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2021.3117131","article-title":"A Spatial-Temporal Feature-Based Detection Framework for Infrared Dim Small Target","volume":"60","author":"J Du","year":"2022","journal-title":"IEEE Trans Geosci Remote Sensing"},{"key":"pone.0361114.ref034","doi-asserted-by":"crossref","unstructured":"Dai Y, Wu Y, Zhou F, Barnard K. Asymmetric Contextual Modulation for Infrared Small Target Detection. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), 2021. 949\u201358. https:\/\/doi.org\/10.1109\/wacv48630.2021.00099","DOI":"10.1109\/WACV48630.2021.00099"},{"key":"pone.0361114.ref035","doi-asserted-by":"crossref","first-page":"121366","DOI":"10.1016\/j.ins.2024.121366","article-title":"LUD-YOLO: A novel lightweight object detection network for unmanned aerial vehicle","volume":"686","author":"Q Fan","year":"2025","journal-title":"Information Sciences"},{"key":"pone.0361114.ref036","first-page":"1","article-title":"LiteYOLO-ID: A Lightweight Object Detection Network for Insulator Defect Detection","volume":"73","author":"D Li","year":"2024","journal-title":"IEEE Trans Instrum Meas"},{"key":"pone.0361114.ref037","doi-asserted-by":"crossref","unstructured":"Chen T, Tan Z, Chu Q. TCI-Former: Thermal Conduction-Inspired Transformer for Infrared Small Target Detection. In: arXiv, 2024.","DOI":"10.1609\/aaai.v38i2.27882"},{"issue":"37","key":"pone.0361114.ref038","first-page":"1","article-title":"Object detection approach to egg tray corner counting with yolov12","author":"P Nakjai","year":"2023","journal-title":"Journal"},{"key":"pone.0361114.ref039","doi-asserted-by":"crossref","unstructured":"Liu J, Fan X, Huang Z, Wu G, Liu R, Zhong W, et al. Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 5792\u2013801. https:\/\/doi.org\/10.1109\/cvpr52688.2022.00571","DOI":"10.1109\/CVPR52688.2022.00571"},{"key":"pone.0361114.ref040","article-title":"Yolov13: Real-time object detection with hypergraph-enhanced adaptive visual perception","author":"M Lei","year":"2025","journal-title":"arXiv preprint"},{"key":"pone.0361114.ref041","doi-asserted-by":"crossref","first-page":"102709","DOI":"10.1016\/j.aei.2024.102709","article-title":"YOLO-MIF: Improved YOLOv8 with Multi-Information fusion for object detection in Gray-Scale images","volume":"62","author":"D Wan","year":"2024","journal-title":"Advanced Engineering Informatics"},{"key":"pone.0361114.ref042","doi-asserted-by":"crossref","first-page":"105539","DOI":"10.1016\/j.infrared.2024.105539","article-title":"YOLO-SGF: Lightweight network for object detection in complex infrared images based on improved YOLOv8","volume":"142","author":"C Guo","year":"2024","journal-title":"Infrared Physics & Technology"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0361114","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T17:26:34Z","timestamp":1792689994000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0361114"}},"subtitle":[],"editor":[{"given":"Yongjie","family":"Li","sequence":"first","affiliation":[{"name":"University of Electronic Science and Technology of China","place":["CHINA"]}],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,10,22]]},"references-count":42,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,10,22]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0361114","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,22]]}}}