{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T16:40:08Z","timestamp":1738255208044,"version":"3.35.0"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T00:00:00Z","timestamp":1734566400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T00:00:00Z","timestamp":1734566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s10489-024-05993-y","type":"journal-article","created":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T07:51:38Z","timestamp":1734594698000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An active object detection model with multi-step prediction based on deep q-learning network and innovative training algorithm"],"prefix":"10.1007","volume":"55","author":[{"given":"Jianyu","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunge","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haibo","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengfei","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,19]]},"reference":[{"issue":"3","key":"5993_CR1","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/JPROC.2023.3238524","volume":"111","author":"Z Zou","year":"2023","unstructured":"Zou Z, Chen K, Shi Z et al (2023) Object Detection in 20 years: A Survey. Proceedings of the IEEE 111(3):257\u2013276. https:\/\/doi.org\/10.1109\/JPROC.2023.3238524","journal-title":"Proceedings of the IEEE"},{"key":"5993_CR2","doi-asserted-by":"publisher","first-page":"105754","DOI":"10.1016\/j.engappai.2022.105754","volume":"119","author":"A Pal","year":"2023","unstructured":"Pal A, Kumar V (2023) AgriDet: Plant Leaf Disease severity classification using agriculture detection framework. Eng Appl Artif Intell 119:105754. https:\/\/doi.org\/10.1016\/j.engappai.2022.105754","journal-title":"Eng Appl Artif Intell"},{"key":"5993_CR3","doi-asserted-by":"publisher","first-page":"10651","DOI":"10.1007\/s10462-023-10438-y","volume":"56","author":"D Zhang","year":"2023","unstructured":"Zhang D, Hao X, Wang D et al (2023) An efficient lightweight convolutional neural network for industrial surface defect detection. Artif Intell Rev 56:10651\u201310677. https:\/\/doi.org\/10.1007\/s10462-023-10438-y","journal-title":"Artif Intell Rev"},{"key":"5993_CR4","doi-asserted-by":"publisher","first-page":"103911","DOI":"10.1016\/j.compind.2023.103911","volume":"148","author":"SB Jha","year":"2023","unstructured":"Jha SB, Babiceanu RF (2023) Deep CNN-based visual defect detection: Survey of current literature. Comput Industry 148:103911. https:\/\/doi.org\/10.1016\/j.compind.2023.103911","journal-title":"Comput Industry"},{"doi-asserted-by":"publisher","unstructured":"Zeng Y, Ma C, Zhu M, et\u00a0al (2021) Cross-Modal 3D Object Detection and Tracking for Auto-Driving. In: 2021 IEEE\/RSJ international conference on intelligent robots and systems (IROS). IEEE, Prague, pp 3850\u20133857, https:\/\/doi.org\/10.1109\/IROS51168.2021.9636498","key":"5993_CR5","DOI":"10.1109\/IROS51168.2021.9636498"},{"issue":"7","key":"5993_CR6","doi-asserted-by":"publisher","first-page":"3781","DOI":"10.1109\/TIV.2023.3264658","volume":"8","author":"L Wang","year":"2023","unstructured":"Wang L, Zhang X, Song Z et al (2023) Multi-Modal 3D Object Detection in Autonomous Driving: A Survey and Taxonomy. IEEE Trans Intell Vehicles 8(7):3781\u20133798. https:\/\/doi.org\/10.1109\/TIV.2023.3264658","journal-title":"IEEE Trans Intell Vehicles"},{"issue":"11","key":"5993_CR7","doi-asserted-by":"publisher","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","volume":"30","author":"ZQ Zhao","year":"2019","unstructured":"Zhao ZQ, Zheng P, Xu ST et al (2019) Object Detection With Deep Learning: A Review. IEEE Trans Neural Netw Learn Syst 30(11):3212\u20133232. https:\/\/doi.org\/10.1109\/TNNLS.2018.2876865","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"5993_CR8","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe DG (2004) Distinctive Image Features from Scale-Invariant Keypoints. Int J Comput Vision 60:91\u2013110. https:\/\/doi.org\/10.1023\/B:VISI.0000029664.99615.94","journal-title":"Int J Comput Vision"},{"doi-asserted-by":"publisher","unstructured":"Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE Computer society conference on computer vision and pattern recognition (CVPR\u201905), vol\u00a01. IEEE, San Diego, pp 886\u2013893, https:\/\/doi.org\/10.1109\/CVPR.2005.177","key":"5993_CR9","DOI":"10.1109\/CVPR.2005.177"},{"key":"5993_CR10","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams CKI et al (2010) The PASCAL Visual Object Classes (VOC) Challenge. Int J Comput Vision 88:303\u2013338. https:\/\/doi.org\/10.1007\/s11263-009-0275-4","journal-title":"Int J Comput Vision"},{"doi-asserted-by":"publisher","unstructured":"Lin TY, Maire M, Belongie S, et\u00a0al (2014) Microsoft COCO: Common objects in context. In: Fleet D, Pajdla T, Schiele B, et\u00a0al (eds) Computer Vision - ECCV 2014. Springer, Cham, Zurich, pp 740\u2013755, https:\/\/doi.org\/10.1007\/978-3-319-10602-1sps48","key":"5993_CR11","DOI":"10.1007\/978-3-319-10602-1sps48"},{"doi-asserted-by":"publisher","unstructured":"Deng J, Dong W, Socher R, et\u00a0al (2009) ImageNet: A large-scale hierarchical image database. In: 2009 IEEE Conference on computer vision and pattern recognition(CVPR). IEEE, Miami, pp 248\u2013255, https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","key":"5993_CR12","DOI":"10.1109\/CVPR.2009.5206848"},{"doi-asserted-by":"publisher","unstructured":"Yang J, Ren Z, Xu M, et\u00a0al (2019) Embodied Amodal Recognition: Learning to Move to Perceive Objects. In: 2019 IEEE\/CVF international conference on computer vision (ICCV). IEEE, Seoul, pp 2040\u20132050, https:\/\/doi.org\/10.1109\/ICCV.2019.00213","key":"5993_CR13","DOI":"10.1109\/ICCV.2019.00213"},{"key":"5993_CR14","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.neunet.2021.10.021","volume":"145","author":"A Ali","year":"2022","unstructured":"Ali A, Zhu Y, Zakarya M (2022) Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction. Neural Netw 145:233\u2013247. https:\/\/doi.org\/10.1016\/j.neunet.2021.10.021","journal-title":"Neural Netw"},{"key":"5993_CR15","doi-asserted-by":"publisher","first-page":"1366","DOI":"10.1007\/s11263-022-01594-9","volume":"130","author":"Y Kong","year":"2022","unstructured":"Kong Y, Fu Y (2022) Human action recognition and prediction: A survey. Int J Comput Vision 130:1366\u20131401. https:\/\/doi.org\/10.1007\/s11263-022-01594-9","journal-title":"Int J Comput Vision"},{"doi-asserted-by":"publisher","unstructured":"Ammirato P, Poirson P, Park E, et\u00a0al (2017) A dataset for developing and benchmarking active vision. In: 2017 IEEE International conference on robotics and automation (ICRA). IEEE, Singapore, pp 1378\u20131385, https:\/\/doi.org\/10.1109\/ICRA.2017.7989164","key":"5993_CR16","DOI":"10.1109\/ICRA.2017.7989164"},{"key":"5993_CR17","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih V, Kavukcuoglu K, Silver D et al (2015) Human-level control through deep reinforcement learning. Nature 518:529\u2013533. https:\/\/doi.org\/10.1038\/nature14236","journal-title":"Nature"},{"issue":"6","key":"5993_CR18","doi-asserted-by":"publisher","first-page":"5984","DOI":"10.1109\/TIE.2021.3090707","volume":"69","author":"S Liu","year":"2022","unstructured":"Liu S, Tian G, Zhang Y et al (2022) Active Object Detection Based on a Novel Deep Q-Learning Network and Long-Term Learning Strategy for the Service Robot. IEEE Trans Industrial Electron 69(6):5984\u20135993. https:\/\/doi.org\/10.1109\/TIE.2021.3090707","journal-title":"IEEE Trans Industrial Electron"},{"doi-asserted-by":"publisher","unstructured":"Ammirato P, Berg AC, Ko\u0161eck\u00e1 J (2018) Active Vision Dataset Benchmark. In: 2018 IEEE\/CVF Conference on computer vision and pattern recognition workshops (CVPRW). IEEE, Anchorage, pp 21270\u201321273. https:\/\/doi.org\/10.1109\/CVPRW.2018.00277","key":"5993_CR19","DOI":"10.1109\/CVPRW.2018.00277"},{"doi-asserted-by":"publisher","unstructured":"Garc\u00eda-Samart\u00edn JF, Ulloa CC, Cerro J, et\u00a0al (2024) Active robotic search for victims using ensemble deep learning techniques. Machine Learning: Science and Technology 5(2). https:\/\/doi.org\/10.1088\/2632-2153\/ad33df","key":"5993_CR20","DOI":"10.1088\/2632-2153\/ad33df"},{"unstructured":"Schaul T, Quan J, Antonoglou I, et\u00a0al (2016) Prioritized Experience Replay arXiv:1511.05952","key":"5993_CR21"},{"key":"5993_CR22","doi-asserted-by":"publisher","first-page":"67319","DOI":"10.1109\/ACCESS.2019.2918703","volume":"7","author":"L Lv","year":"2019","unstructured":"Lv L, Zhang S, Ding D et al (2019) Path Planning via an Improved DQN-Based Learning Policy. IEEE Access 7:67319\u201367330. https:\/\/doi.org\/10.1109\/ACCESS.2019.2918703","journal-title":"IEEE Access"},{"issue":"12","key":"5993_CR23","doi-asserted-by":"publisher","first-page":"7363","DOI":"10.1109\/TSMC.2020.2967936","volume":"51","author":"J Sharma","year":"2021","unstructured":"Sharma J, Andersen PA, Granmo OC et al (2021) Deep Q-Learning With Q-Matrix Transfer Learning for Novel Fire Evacuation Environment. IEEE Trans Syst, Man, Cybernetics: Syst 51(12):7363\u20137381. https:\/\/doi.org\/10.1109\/TSMC.2020.2967936","journal-title":"IEEE Trans Syst, Man, Cybernetics: Syst"},{"issue":"10","key":"5993_CR24","doi-asserted-by":"publisher","first-page":"11870","DOI":"10.1109\/JSEN.2020.3030791","volume":"21","author":"HY Lin","year":"2021","unstructured":"Lin HY, Liang SC, Chen YK (2021) Robotic Grasping With Multi-View Image Acquisition and Model-Based Pose Estimation. IEEE Sensors J 21(10):11870\u201311878. https:\/\/doi.org\/10.1109\/JSEN.2020.3030791","journal-title":"IEEE Sensors J"},{"issue":"1","key":"5993_CR25","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1109\/TRO.2021.3083197","volume":"38","author":"S Song","year":"2022","unstructured":"Song S, Kim D, Choi S (2022) View Path Planning via Online Multiview Stereo for 3-D Modeling of Large-Scale Structures. IEEE Trans Robotics 38(1):372\u2013390. https:\/\/doi.org\/10.1109\/TRO.2021.3083197","journal-title":"IEEE Trans Robotics"},{"doi-asserted-by":"publisher","unstructured":"Morrison D, Corke P, Leitner J (2019) Multi-View Picking: Next-best-view Reaching for Improved Grasping in Clutter. In: 2019 International conference on robotics and automation (ICRA). IEEE, Montreal, pp 8762\u20138768. https:\/\/doi.org\/10.1109\/ICRA.2019.8793805","key":"5993_CR26","DOI":"10.1109\/ICRA.2019.8793805"},{"doi-asserted-by":"publisher","unstructured":"Lehnert C, Tsai D, Eriksson A, et\u00a0al (2019) 3D Move to See: Multi-perspective visual servoing towards the next best view within unstructured and occluded environments. In: 2019 IEEE\/RSJ International conference on intelligent robots and systems (IROS). IEEE, Macao, pp 3890\u20133897, https:\/\/doi.org\/10.1109\/IROS40897.2019.8967918","key":"5993_CR27","DOI":"10.1109\/IROS40897.2019.8967918"},{"key":"5993_CR28","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.biosystemseng.2023.06.003","volume":"231","author":"D Rapado-Rinc\u00f3n","year":"2023","unstructured":"Rapado-Rinc\u00f3n D, van Henten EJ, Kootstra G (2023) Development and evaluation of automated localisation and reconstruction of all fruits on tomato plants in a greenhouse based on multi-view perception and 3D multi-object tracking. Biosyst Eng 231:78\u201391. https:\/\/doi.org\/10.1016\/j.biosystemseng.2023.06.003","journal-title":"Biosyst Eng"},{"issue":"2","key":"5993_CR29","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1109\/34.982896","volume":"24","author":"J Denzler","year":"2002","unstructured":"Denzler J, Brown C (2002) Information theoretic sensor data selection for active object recognition and state estimation. IEEE Trans Pattern Anal Mach Intell 24(2):145\u2013157. https:\/\/doi.org\/10.1109\/34.982896","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"5993_CR30","doi-asserted-by":"publisher","first-page":"1198","DOI":"10.1109\/TRO.2014.2334912","volume":"30","author":"H van Hoof","year":"2014","unstructured":"van Hoof H, Kroemer O, Peters J (2014) Probabilistic Segmentation and Targeted Exploration of Objects in Cluttered Environments. IEEE Trans Robot 30(5):1198\u20131209. https:\/\/doi.org\/10.1109\/TRO.2014.2334912","journal-title":"IEEE Trans Robot"},{"doi-asserted-by":"publisher","unstructured":"Yang J, Waslander SL (2022) Next-Best-View Prediction for Active Stereo Cameras and Highly Reflective Objects. In: 2022 International conference on robotics and automation (ICRA). IEEE, Philadelphia, pp 3684\u20133690. https:\/\/doi.org\/10.1109\/ICRA46639.2022.9811917","key":"5993_CR31","DOI":"10.1109\/ICRA46639.2022.9811917"},{"key":"5993_CR32","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1007\/s12559-022-10030-6","volume":"15","author":"H Cheng","year":"2023","unstructured":"Cheng H, Duan F, He M (2023) Spiking Memory Policy with Population-encoding for Partially Observable Markov Decision Process Problems. Cognitive Comput 15:1153\u20131166. https:\/\/doi.org\/10.1007\/s12559-022-10030-6","journal-title":"Cognitive Comput"},{"doi-asserted-by":"publisher","unstructured":"Zhang H, Liu H, Guo D, et\u00a0al (2017) From foot to head: Active face finding using deep q-learning. In: 2017 IEEE International conference on image processing (ICIP). IEEE, Beijing, pp 1862\u20131866. https:\/\/doi.org\/10.1109\/ICIP.2017.8296604","key":"5993_CR33","DOI":"10.1109\/ICIP.2017.8296604"},{"doi-asserted-by":"publisher","unstructured":"Han X, Liu H, Sun F, et\u00a0al (2018) Active Object Detection Using Double DQN and Prioritized Experience Replay. In: 2018 International joint conference on neural networks (IJCNN). IEEE, Rio de Janeiro, pp 1\u20137. https:\/\/doi.org\/10.1109\/IJCNN.2018.8489296","key":"5993_CR34","DOI":"10.1109\/IJCNN.2018.8489296"},{"issue":"1","key":"5993_CR35","doi-asserted-by":"publisher","first-page":"2094","DOI":"10.1609\/aaai.v30i1.10295","volume":"30","author":"H Van Hasselt","year":"2016","unstructured":"Van Hasselt H, Guez A, Silver D (2016) Deep Reinforcement Learning with Double Q-Learning. Proceed AAAI Conference Artif Intell 30(1):2094\u20132100. https:\/\/doi.org\/10.1609\/aaai.v30i1.10295","journal-title":"Proceed AAAI Conference Artif Intell"},{"issue":"6","key":"5993_CR36","doi-asserted-by":"publisher","first-page":"3723","DOI":"10.1109\/TII.2019.2890849","volume":"15","author":"X Han","year":"2019","unstructured":"Han X, Liu H, Sun F et al (2019) Active Object Detection With Multistep Action Prediction Using Deep Q-Network. IEEE Trans Industrial Inf 15(6):3723\u20133731. https:\/\/doi.org\/10.1109\/TII.2019.2890849","journal-title":"IEEE Trans Industrial Inf"},{"doi-asserted-by":"publisher","unstructured":"Xu Q, Fang F, Gauthier N, et\u00a0al (2021) Towards Efficient Multiview Object Detection with Adaptive Action Prediction. In: 2021 IEEE international conference on robotics and automation (ICRA). IEEE, Xi\u2019an, pp 13423\u201313429. https:\/\/doi.org\/10.1109\/ICRA48506.2021.9561388","key":"5993_CR37","DOI":"10.1109\/ICRA48506.2021.9561388"},{"doi-asserted-by":"publisher","unstructured":"Fang F, Xu Q, Gauthier N, et\u00a0al (2021) Enhancing Multi-Step Action Prediction for Active Object Detection. In: 2021 IEEE International conference on image processing (ICIP). IEEE, Anchorage, pp 2189\u20132193. https:\/\/doi.org\/10.1109\/ICIP42928.2021.9506078","key":"5993_CR38","DOI":"10.1109\/ICIP42928.2021.9506078"},{"doi-asserted-by":"publisher","unstructured":"Schmid JF, Lauri M, Frintrop S (2019) Explore, Approach, and Terminate: Evaluating Subtasks in Active Visual Object Search Based on Deep Reinforcement Learning. In: 2019 IEEE\/RSJ International conference on intelligent robots and systems (IROS). IEEE, Macau, pp 5008\u20135013, https:\/\/doi.org\/10.1109\/IROS40897.2019.8967805","key":"5993_CR39","DOI":"10.1109\/IROS40897.2019.8967805"},{"doi-asserted-by":"publisher","unstructured":"Peng W, Wang W, Wang Y, et\u00a0al (2024) Key Technologies and Trends of Active Robotic 3-D Measurement in Intelligent Manufacturing. IEEE\/ASME Trans Mechatron pp 1\u201322. https:\/\/doi.org\/10.1109\/TMECH.2024.3396222","key":"5993_CR40","DOI":"10.1109\/TMECH.2024.3396222"},{"issue":"3","key":"5993_CR41","doi-asserted-by":"publisher","first-page":"3357","DOI":"10.1109\/TASE.2023.3278994","volume":"21","author":"J Akl","year":"2024","unstructured":"Akl J, Alladkani F, Calli B (2024) Feature-Driven Next View Planning for Cutting Path Generation in Robotic Metal Scrap Recycling. IEEE Trans Automation Sci Eng 21(3):3357\u20133373. https:\/\/doi.org\/10.1109\/TASE.2023.3278994","journal-title":"IEEE Trans Automation Sci Eng"},{"key":"5993_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patrec.2024.05.014","volume":"184","author":"T Wang","year":"2024","unstructured":"Wang T, Xi W, Cheng Y et al (2024) RL-NBV: A deep reinforcement learning based next-best-view method for unknown object reconstruction. Pattern Recognition Lett 184:1\u20136. https:\/\/doi.org\/10.1016\/j.patrec.2024.05.014","journal-title":"Pattern Recognition Lett"},{"unstructured":"Wang A, Chen H, Liu L, et\u00a0al (2024) Yolov10: Real-time End-to-End Object Detection arXiv:2405.14458","key":"5993_CR43"},{"key":"5993_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1609\/aaai.v32i1.11798","volume":"32","author":"A Tavakoli","year":"2018","unstructured":"Tavakoli A, Pardo F, Kormushev P (2018) Action Branching Architectures for Deep Reinforcement Learning. Proceed AAAI Conference Artif Intell 32:1\u20139. https:\/\/doi.org\/10.1609\/aaai.v32i1.11798","journal-title":"Proceed AAAI Conference Artif Intell"},{"doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, et\u00a0al (2016) Deep Residual Learning for Image Recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). IEEE, Las Vegas, pp 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","key":"5993_CR45","DOI":"10.1109\/CVPR.2016.90"},{"unstructured":"Wang Z, Schaul T, Hessel M, et\u00a0al (2016) Dueling Network Architectures for Deep Reinforcement Learning. In: Proceedings of The 33rd international conference on machine learning. PMLR, New York, pp 1995\u20132003","key":"5993_CR46"},{"key":"5993_CR47","doi-asserted-by":"publisher","first-page":"1093132","DOI":"10.3389\/fnbot.2023.1093132","volume":"17","author":"H Sun","year":"2023","unstructured":"Sun H, Zhu F, Li Y et al (2023) Viewpoint planning with transition management for active object recognition. Front Neurorobot 17:1093132. https:\/\/doi.org\/10.3389\/fnbot.2023.1093132","journal-title":"Front Neurorobot"},{"doi-asserted-by":"publisher","unstructured":"Sun H, Zhu F, Kong Y, et\u00a0al (2021) Continuous Viewpoint Planning in Conjunction with Dynamic Exploration for Active Object Recognition. Entropy 23(12). https:\/\/doi.org\/10.3390\/e23121702","key":"5993_CR48","DOI":"10.3390\/e23121702"},{"issue":"7","key":"5993_CR49","doi-asserted-by":"publisher","first-page":"3034","DOI":"10.1109\/TNNLS.2020.3009214","volume":"32","author":"N Wang","year":"2021","unstructured":"Wang N, Gao Y, Zhao H et al (2021) Reinforcement Learning-Based Optimal Tracking Control of an Unknown Unmanned Surface Vehicle. IEEE Trans Neural Netw Learn Syst 32(7):3034\u20133045. https:\/\/doi.org\/10.1109\/TNNLS.2020.3009214","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"12","key":"5993_CR50","doi-asserted-by":"publisher","first-page":"9878","DOI":"10.1109\/TIE.2018.2878157","volume":"66","author":"H Liu","year":"2019","unstructured":"Liu H, Sun F, Zhang X (2019) Robotic Material Perception Using Active Multimodal Fusion. IEEE Trans Industrial Electron 66(12):9878\u20139886. https:\/\/doi.org\/10.1109\/TIE.2018.2878157","journal-title":"IEEE Trans Industrial Electron"},{"doi-asserted-by":"publisher","unstructured":"Singh A, Sha J, Narayan KS, et\u00a0al (2014) BigBIRD: A large-scale 3D database of object instances. In: 2014 IEEE International conference on robotics and automation (ICRA). IEEE, Miami, pp 509\u2013516. https:\/\/doi.org\/10.1109\/ICRA.2014.6906903","key":"5993_CR51","DOI":"10.1109\/ICRA.2014.6906903"},{"doi-asserted-by":"publisher","unstructured":"Wang X, Wang S, Liang X et al (2024) Deep Reinforcement Learning: A Survey. IEEE Trans Neural Netw Learn Syst 35(4):5064\u20135078. https:\/\/doi.org\/10.1109\/TNNLS.2022.3207346","key":"5993_CR52","DOI":"10.1109\/TNNLS.2022.3207346"},{"key":"5993_CR53","doi-asserted-by":"publisher","first-page":"60836","DOI":"10.1109\/ACCESS.2022.3179720","volume":"10","author":"D F\u00e4hrmann","year":"2022","unstructured":"F\u00e4hrmann D, Jorek N, Damer N et al (2022) Double Deep Q-Learning With Prioritized Experience Replay for Anomaly Detection in Smart Environments. IEEE Access 10:60836\u201360848. https:\/\/doi.org\/10.1109\/ACCESS.2022.3179720","journal-title":"IEEE Access"},{"doi-asserted-by":"publisher","unstructured":"Chen Y, Liang L (2023) SLP-Improved DDPG Path-Planning Algorithm for Mobile Robot in Large-Scale Dynamic Environment. Sensors 23(7). https:\/\/doi.org\/10.3390\/s23073521","key":"5993_CR54","DOI":"10.3390\/s23073521"},{"issue":"4","key":"5993_CR55","doi-asserted-by":"publisher","first-page":"10224","DOI":"10.1109\/LRA.2022.3193019","volume":"7","author":"F Fang","year":"2022","unstructured":"Fang F, Liang W, Wu Y et al (2022) Self-Supervised Reinforcement Learning for Active Object Detection. IEEE Robot Automation Lett 7(4):10224\u201310231. https:\/\/doi.org\/10.1109\/LRA.2022.3193019","journal-title":"IEEE Robot Automation Lett"},{"doi-asserted-by":"publisher","unstructured":"Yang N, Lu F, Yu B, et\u00a0al (2023) Service Robot Active Object Detection based on Spatial Exploration using Deep Recurrent Q-learning Network. In: 2023 IEEE International conference on robotics and biomimetics (ROBIO), pp 1\u20136. https:\/\/doi.org\/10.1109\/ROBIO58561.2023.10354931","key":"5993_CR56","DOI":"10.1109\/ROBIO58561.2023.10354931"},{"key":"5993_CR57","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.neucom.2021.09.037","volume":"466","author":"N Xu","year":"2021","unstructured":"Xu N, Huo C, Zhang X et al (2021) Dynamic camera configuration learning for high-confidence active object detection. Neurocomputing 466:113\u2013127. https:\/\/doi.org\/10.1016\/j.neucom.2021.09.037","journal-title":"Neurocomputing"},{"issue":"4","key":"5993_CR58","doi-asserted-by":"publisher","first-page":"1922","DOI":"10.1109\/TPAMI.2020.3032166","volume":"44","author":"Z Tian","year":"2022","unstructured":"Tian Z, Shen C, Chen H et al (2022) FCOS: A Simple and Strong Anchor-Free Object Detector. IEEE Trans Pattern Anal Mach Intell 44(4):1922\u20131933. https:\/\/doi.org\/10.1109\/TPAMI.2020.3032166","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5993_CR59","doi-asserted-by":"publisher","first-page":"1501","DOI":"10.1007\/s00366-023-01852-5","volume":"40","author":"A Abbaszadeh Shahri","year":"2024","unstructured":"Abbaszadeh Shahri A, Chunling S, Larsson S (2024) A hybrid ensemble-based automated deep learning approach to generate 3D geo-models and uncertainty analysis. Eng Comput 40:1501\u20131516. https:\/\/doi.org\/10.1007\/s00366-023-01852-5","journal-title":"Eng Comput"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05993-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05993-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05993-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T16:03:57Z","timestamp":1738253037000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05993-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,19]]},"references-count":59,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["5993"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05993-y","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2024,12,19]]},"assertion":[{"value":"30 September 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 December 2024","order":2,"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 they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest\/Competing Interests"}}],"article-number":"185"}}