{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T11:13:10Z","timestamp":1770462790173,"version":"3.49.0"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T00:00:00Z","timestamp":1769126400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T00:00:00Z","timestamp":1769126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The QUTJBZ Program","award":["2025ZDZX17"],"award-info":[{"award-number":["2025ZDZX17"]}]},{"name":"The QUTJBZ Program","award":["2025ZDZX17"],"award-info":[{"award-number":["2025ZDZX17"]}]},{"name":"The QUTJBZ Program","award":["2025ZDZX17"],"award-info":[{"award-number":["2025ZDZX17"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s11554-025-01850-4","type":"journal-article","created":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T17:52:50Z","timestamp":1769190770000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FHG-DETR: a lightweight model for detecting the ripeness of color-changing melons"],"prefix":"10.1007","volume":"23","author":[{"given":"Wanglin","family":"Zheng","sequence":"first","affiliation":[]},{"given":"Qing","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Gongjun","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Yunhui","family":"Luo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,23]]},"reference":[{"key":"1850_CR1","doi-asserted-by":"publisher","unstructured":"Vaswani, A.: Attention is all you need. Adv. Neural Inf. Process. Syst. (2017) https:\/\/doi.org\/10.48550\/arXiv.1706.03762","DOI":"10.48550\/arXiv.1706.03762"},{"key":"1850_CR2","doi-asserted-by":"publisher","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proceedings of ECCV, pp. 213\u2013229. Springer (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_13","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"1850_CR3","doi-asserted-by":"publisher","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., Chen, J.: Detrs beat yolos on real-time object detection. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16965\u201316974 (2024). https:\/\/doi.org\/10.1109\/CVPR52733.2024.01605","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"1850_CR4","doi-asserted-by":"publisher","unstructured":"Chen, J., Kao, S., He, H., Zhuo, W., Wen, S., Lee, C., Chan, S.H.G.: Run, don\u2019t walk: chasing higher flops for faster neural networks. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12021\u201312031 (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.01157","DOI":"10.1109\/CVPR52729.2023.01157"},{"key":"1850_CR5","doi-asserted-by":"publisher","unstructured":"Pan, Z., Cai, J., Zhuang, B.: Fast vision transformers with hilo attention. Adv. Neural Inf. Process. Syst. 35, 14541\u201314554 (2022) https:\/\/doi.org\/10.48550\/arXiv.2205.13213","DOI":"10.48550\/arXiv.2205.13213"},{"key":"1850_CR6","doi-asserted-by":"publisher","first-page":"3244","DOI":"10.1007\/s11947-011-0694-4","volume":"5","author":"C Lang","year":"2012","unstructured":"Lang, C., H\u00fcbert, T.: A colour ripeness indicator for apples. Food Bioprocess Technol. 5, 3244\u20133249 (2012). https:\/\/doi.org\/10.1007\/s11947-011-0694-4","journal-title":"Food Bioprocess Technol."},{"issue":"1","key":"1850_CR7","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.foodres.2008.09.007","volume":"42","author":"M Taniwaki","year":"2009","unstructured":"Taniwaki, M., Takahashi, M., Sakurai, N.: Determination of optimum ripeness for edibility of postharvest melons using nondestructive vibration. Food Res. Int. 42(1), 137\u2013141 (2009). https:\/\/doi.org\/10.1016\/j.foodres.2008.09.007","journal-title":"Food Res. Int."},{"key":"1850_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2022.132599","volume":"363","author":"J Zhang","year":"2022","unstructured":"Zhang, J., Wang, X., Xia, J., Xing, S., Zhang, X.: Flexible sensing enabled intelligent manipulator system (fsims) for avocados (Persea Americana Mill) ripeness grading. J. Clean. Prod. 363, 132599 (2022). https:\/\/doi.org\/10.1016\/j.jclepro.2022.132599","journal-title":"J. Clean. Prod."},{"issue":"4","key":"1850_CR9","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1016\/j.jfoodeng.2012.07.008","volume":"113","author":"MHM Hazir","year":"2012","unstructured":"Hazir, M.H.M., Shariff, A.R.M., Amiruddin, M.D., Ramli, A.R., Saripan, M.I.: Oil palm bunch ripeness classification using fluorescence technique. J. Food Eng. 113(4), 534\u2013540 (2012). https:\/\/doi.org\/10.1016\/j.jfoodeng.2012.07.008","journal-title":"J. Food Eng."},{"key":"1850_CR10","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1016\/j.talanta.2015.06.058","volume":"144","author":"V Giovenzana","year":"2015","unstructured":"Giovenzana, V., Civelli, R., Beghi, R., Oberti, R., Guidetti, R.: Testing of a simplified led based vis\/nir system for rapid ripeness evaluation of white grape (Vitis Vinifera L.) for franciacorta wine. Talanta 144, 584\u2013591 (2015). https:\/\/doi.org\/10.1016\/j.talanta.2015.06.058","journal-title":"Talanta"},{"issue":"22","key":"1850_CR11","doi-asserted-by":"publisher","first-page":"22192","DOI":"10.1109\/JSEN.2022.3210439","volume":"22","author":"V Maharshi","year":"2022","unstructured":"Maharshi, V., Sharma, S., Prajesh, R., Das, S., Agarwal, A., Mitra, B.: A novel sensor for fruit ripeness estimation using lithography free approach. IEEE Sens. J. 22(22), 22192\u201322199 (2022). https:\/\/doi.org\/10.1109\/JSEN.2022.3210439","journal-title":"IEEE Sens. J."},{"key":"1850_CR12","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2023.3315136","author":"E Zou","year":"2023","unstructured":"Zou, E., Fang, C., Song, D.: A low-cost handheld photoacoustic (pa) probe for rapid and non-destructive detection of watermelon ripeness. IEEE Sens. J. (2023). https:\/\/doi.org\/10.1109\/JSEN.2023.3315136","journal-title":"IEEE Sens. J."},{"key":"1850_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2023.102210","volume":"77","author":"J Li","year":"2023","unstructured":"Li, J., Zhu, Z., Liu, H., Su, Y., Deng, L.: Strawberry r-cnn: recognition and counting model of strawberry based on improved faster r-cnn. Ecol. Inform. 77, 102210 (2023). https:\/\/doi.org\/10.1016\/j.ecoinf.2023.102210","journal-title":"Ecol. Inform."},{"key":"1850_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2023.108605","volume":"217","author":"C Li","year":"2024","unstructured":"Li, C., Lin, J., Li, Z., Mai, C., Jiang, R., Li, J.: An efficient detection method for litchi fruits in a natural environment based on improved yolov7-litchi. Comput. Electron. Agric. 217, 108605 (2024). https:\/\/doi.org\/10.1016\/j.compag.2023.108605","journal-title":"Comput. Electron. Agric."},{"key":"1850_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.infrared.2024.105487","volume":"141","author":"L Cao","year":"2024","unstructured":"Cao, L., Wang, Q., Luo, Y., Hou, Y., Cao, J., Zheng, W.: Yolo-tsl: a lightweight target detection algorithm for uav infrared images based on triplet attention and slim-neck. Infrared Phys. Technol. 141, 105487 (2024). https:\/\/doi.org\/10.1016\/j.infrared.2024.105487","journal-title":"Infrared Phys. Technol."},{"issue":"1","key":"1850_CR16","doi-asserted-by":"publisher","first-page":"14400","DOI":"10.1038\/s41598-024-65293-w","volume":"14","author":"G Chen","year":"2024","unstructured":"Chen, G., Hou, Y., Cui, T., Li, H., Shangguan, F., Cao, L.: Yolov8-cml: a lightweight target detection method for color-changing melon ripening in intelligent agriculture. Sci. Rep. 14(1), 14400 (2024). https:\/\/doi.org\/10.1038\/s41598-024-65293-w","journal-title":"Sci. Rep."},{"key":"1850_CR17","doi-asserted-by":"publisher","unstructured":"Dosovitskiy, A.: An image is worth 16 $$\\times$$ 16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020). https:\/\/doi.org\/10.48550\/arXiv.2010.11929","DOI":"10.48550\/arXiv.2010.11929"},{"issue":"19","key":"1850_CR18","doi-asserted-by":"publisher","first-page":"22488","DOI":"10.1007\/s10489-023-04799-8","volume":"53","author":"B Xiao","year":"2023","unstructured":"Xiao, B., Nguyen, M., Yan, W.Q.: Fruit ripeness identification using transformers. Appl. Intell. 53(19), 22488\u201322499 (2023). https:\/\/doi.org\/10.1007\/s10489-023-04799-8","journal-title":"Appl. Intell."},{"key":"1850_CR19","doi-asserted-by":"publisher","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"1850_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.atech.2023.100196","volume":"4","author":"R Shinoda","year":"2023","unstructured":"Shinoda, R., Kataoka, H., Hara, K., Noguchi, R.: Transformer-based ripeness segmentation for tomatoes. Smart Agric. Technol. 4, 100196 (2023). https:\/\/doi.org\/10.1016\/j.atech.2023.100196","journal-title":"Smart Agric. Technol."},{"key":"1850_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2024.108674","volume":"218","author":"H Liu","year":"2024","unstructured":"Liu, H., Wang, X., Zhao, F., Yu, F., Lin, P., Gan, Y., Ren, X., Chen, Y., Tu, J.: Upgrading swin-b transformer-based model for accurately identifying ripe strawberries by coupling task-aligned one-stage object detection mechanism. Comput. Electron. Agric. 218, 108674 (2024). https:\/\/doi.org\/10.1016\/j.compag.2024.108674","journal-title":"Comput. Electron. Agric."},{"key":"1850_CR22","doi-asserted-by":"publisher","unstructured":"Lin, H., Liu, J., Li, X., Wei, L., Liu, Y., Han, B., Wu, Z.: Dcea: Detr with concentrated deformable attention for end-to-end ship detection in sar images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. (2024). https:\/\/doi.org\/10.1109\/JSTARS.2024.3461723","DOI":"10.1109\/JSTARS.2024.3461723"},{"key":"1850_CR23","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2024.3357775","author":"Y Jin","year":"2024","unstructured":"Jin, Y., Zhu, X., Yue, Y., Lim, E.G., Wang, W.: Cr-dino: a novel camera-radar fusion 2d object detection model based on transformer. IEEE Sens. J. (2024). https:\/\/doi.org\/10.1109\/JSEN.2024.3357775","journal-title":"IEEE Sens. J."},{"key":"1850_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2024.109014","volume":"221","author":"C Guo","year":"2024","unstructured":"Guo, C., Zhu, C., Liu, Y., Huang, R., Cao, B., Zhu, Q., Zhang, R., Zhang, B.: End-to-end lightweight transformer-based neural network for grasp detection towards fruit robotic handling. Comput. Electron. Agric. 221, 109014 (2024). https:\/\/doi.org\/10.1016\/j.compag.2024.109014","journal-title":"Comput. Electron. Agric."},{"key":"1850_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2024.108788","volume":"219","author":"H Li","year":"2024","unstructured":"Li, H., Shi, F.: A detr-like detector-based semi-supervised object detection method for brassica chinensis growth monitoring. Comput. Electron. Agric. 219, 108788 (2024). https:\/\/doi.org\/10.1016\/j.compag.2024.108788","journal-title":"Comput. Electron. Agric."},{"key":"1850_CR26","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.aiia.2023.02.004","volume":"7","author":"M Rizzo","year":"2023","unstructured":"Rizzo, M., Marcuzzo, M., Zangari, A., Gasparetto, A., Albarelli, A.: Fruit ripeness classification: a survey. Artif. Intell. Agric. 7, 44\u201357 (2023). https:\/\/doi.org\/10.1016\/j.aiia.2023.02.004","journal-title":"Artif. Intell. Agric."},{"key":"1850_CR27","doi-asserted-by":"publisher","first-page":"1415297","DOI":"10.3389\/fpls.2024.1415297","volume":"15","author":"S Wang","year":"2024","unstructured":"Wang, S., Jiang, H., Yang, J., Ma, X., Chen, J., Li, Z., Tang, X.: Lightweight tomato ripeness detection algorithm based on the improved rt-detr. Front. Plant Sci. 15, 1415297 (2024). https:\/\/doi.org\/10.3389\/fpls.2024.1415297","journal-title":"Front. Plant Sci."},{"key":"1850_CR28","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., Le, Q.V.: EfficientDet: scalable and efficient object detection (2020). arXiv:1911.09070","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"1850_CR29","doi-asserted-by":"crossref","unstructured":"Hua, B.-S., Tran, M.-K., Yeung, S.-K.: Pointwise convolutional neural networks (2018). arXiv:1712.05245","DOI":"10.1109\/CVPR.2018.00109"},{"key":"1850_CR30","doi-asserted-by":"publisher","unstructured":"Li, H., Li, J., Wei, H., Liu, Z., Zhan, Z., Ren, Q.: Slim-neck by gsconv: A better design paradigm of detector architectures for autonomous vehicles. arXiv preprint arXiv:2206.02424 (2022). https:\/\/doi.org\/10.48550\/arXiv.2206.02424","DOI":"10.48550\/arXiv.2206.02424"},{"key":"1850_CR31","doi-asserted-by":"publisher","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800\u20131807. IEEE, Honolulu, HI, USA (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.195","DOI":"10.1109\/CVPR.2017.195"},{"key":"1850_CR32","doi-asserted-by":"publisher","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., et\u00a0al.: Searching for mobilenetv3. In: Proceedings of IEEE\/CVF International Conference on Computer Vision, pp. 1314\u20131324 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00140","DOI":"10.1109\/ICCV.2019.00140"},{"key":"1850_CR33","doi-asserted-by":"publisher","unstructured":"Liu, X., Peng, H., Zheng, N., Yang, Y., Hu, H., Yuan, Y.: Efficientvit: memory efficient vision transformer with cascaded group attention. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14420\u201314430 (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.01386","DOI":"10.1109\/CVPR52729.2023.01386"},{"key":"1850_CR34","doi-asserted-by":"publisher","unstructured":"Liu, Z., Mao, H., Wu, C., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976\u201311986 (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.01167","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"1850_CR35","doi-asserted-by":"publisher","unstructured":"Li, Y., Hu, J., Wen, Y., Evangelidis, G., Salahi, K., Wang, Y., Tulyakov, S., Ren, J.: Rethinking vision transformers for mobilenet size and speed. In: Proceedings of IEEE\/CVF International Conference on Computer Vision, pp. 16889\u201316900 (2023). https:\/\/doi.org\/10.1109\/ICCV51070.2023.01549","DOI":"10.1109\/ICCV51070.2023.01549"},{"key":"1850_CR36","doi-asserted-by":"publisher","unstructured":"Wang, A., Chen, H., Lin, Z., Han, J., Ding, G.: Repvit: Revisiting mobile cnn from vit perspective. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15909\u201315920 (2024). https:\/\/doi.org\/10.1109\/CVPR52733.2024.01506","DOI":"10.1109\/CVPR52733.2024.01506"},{"key":"1850_CR37","doi-asserted-by":"publisher","unstructured":"Zhang, T., Li, L., Zhou, Y., Liu, W., Qian, C., Ji, X.: Cas-vit: Convolutional additive self-attention vision transformers for efficient mobile applications. arXiv preprint arXiv:2408.03703 (2024). https:\/\/doi.org\/10.48550\/arXiv.2408.03703","DOI":"10.48550\/arXiv.2408.03703"},{"key":"1850_CR38","doi-asserted-by":"publisher","unstructured":"Sun, S., Ren, W., Gao, X., Wang, R., Cao, X.: Restoring images in adverse weather conditions via histogram transformer. In: Proceedings of ECCV, pp. 111\u2013129. Springer (2025). https:\/\/doi.org\/10.1007\/978-3-031-72670-5_7","DOI":"10.1007\/978-3-031-72670-5_7"},{"key":"1850_CR39","doi-asserted-by":"publisher","unstructured":"Xia, Z., Pan, X., Song, S., Li, L.E., Huang, G.: Vision transformer with deformable attention. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4794\u20134803 (2022).https:\/\/doi.org\/10.1109\/CVPR52688.2022.00475","DOI":"10.1109\/CVPR52688.2022.00475"},{"key":"1850_CR40","doi-asserted-by":"publisher","unstructured":"Shaker, A., Maaz, M., Rasheed, H., Khan, S., Yang, M., Khan, F.S.: Swiftformer: efficient additive attention for transformer-based real-time mobile vision applications. In: Proceedings of IEEE\/CVF International Conference on Computer Vision, pp. 17425\u201317436 (2023). https:\/\/doi.org\/10.1109\/ICCV51070.2023.01598","DOI":"10.1109\/ICCV51070.2023.01598"},{"key":"1850_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.107917","volume":"170","author":"Y Chen","year":"2024","unstructured":"Chen, Y., Zhang, C., Chen, B., Huang, Y., Sun, Y., Wang, C., Fu, X., Dai, Y., Qin, F., Peng, Y., et al.: Accurate leukocyte detection based on deformable-detr and multi-level feature fusion for aiding diagnosis of blood diseases. Comput. Biol. Med. 170, 107917 (2024). https:\/\/doi.org\/10.1016\/j.compbiomed.2024.107917","journal-title":"Comput. Biol. Med."},{"key":"1850_CR42","unstructured":"Joseph: ripeness-fruit Dataset. Roboflow. https:\/\/universe.roboflow.com\/joseph-xv9qq\/ripeness-fruit. Accessed 02 Oct 2025"},{"issue":"6","key":"1850_CR43","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2016","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2016). https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1850_CR44","doi-asserted-by":"publisher","unstructured":"Wang, C., Yeh, I., Liao, H.Y.M.: Yolov9: Learning what you want to learn using programmable gradient information. In: Proceedings of ECCV, pp. 1\u201321. Springer (2025). https:\/\/doi.org\/10.48550\/arXiv.2402.13616","DOI":"10.48550\/arXiv.2402.13616"},{"key":"1850_CR45","doi-asserted-by":"publisher","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., Ding, G.: Yolov10: real-time end-to-end object detection. arXiv preprint arXiv:2405.14458 (2024). https:\/\/doi.org\/10.48550\/arXiv.2405.14458","DOI":"10.48550\/arXiv.2405.14458"},{"key":"1850_CR46","doi-asserted-by":"publisher","unstructured":"Khanam, R., Hussain, M.: Yolov11: An overview of the key architectural enhancements. arXiv preprint arXiv:2410.17725 (2024). https:\/\/doi.org\/10.48550\/arXiv.2410.17725","DOI":"10.48550\/arXiv.2410.17725"},{"key":"1850_CR47","doi-asserted-by":"publisher","unstructured":"Feng, C., Zhong, Y., Gao, Y., Scott, M.R., Huang, W.: Tood: task-aligned one-stage object detection. In: Proceedings of IEEE\/CVF International Conference on Computer Vision, pp. 3490\u20133499 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00349 . IEEE Computer Society","DOI":"10.1109\/ICCV48922.2021.00349"},{"key":"1850_CR48","doi-asserted-by":"publisher","unstructured":"Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Adv. Neural Inf. Process. Syst. 33, 21002\u201321012 (2020). https:\/\/doi.org\/10.48550\/arXiv.2006.04388","DOI":"10.48550\/arXiv.2006.04388"},{"key":"1850_CR49","doi-asserted-by":"publisher","unstructured":"Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., Ni, L.M., Shum, H.Y.: Dino: Detr with improved denoising anchor boxes for end-to-end object detection. arXiv preprint arXiv:2203.03605 (2022). https:\/\/doi.org\/10.48550\/arXiv.2203.03605","DOI":"10.48550\/arXiv.2203.03605"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01850-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-025-01850-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-025-01850-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T16:49:15Z","timestamp":1770396555000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-025-01850-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,23]]},"references-count":49,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["1850"],"URL":"https:\/\/doi.org\/10.1007\/s11554-025-01850-4","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"value":"1861-8200","type":"print"},{"value":"1861-8219","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,23]]},"assertion":[{"value":"3 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study did not involve any human participants or animals. Ethics approval and consent to participate are not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All authors have agreed on the content, provided explicit consent for submission, and obtained approval from the responsible authorities at their institute prior to the submission of the work.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"All materials used in this study are available upon reasonable request from the corresponding author.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Materials availability"}}],"article-number":"56"}}