Dr Feras Dayoub
Senior Lecturer
School of Computer and Mathematical Sciences
Faculty of Sciences, Engineering and Technology
Eligible to supervise Masters and PhD - email supervisor to discuss availability.
I am a Senior Lecturer with the School of Computer Science and the Australian Institute for Machine Learning (AIML). I am also an Associate Investigator with QUT Centre for Robotics (QCR). I served as a Chief Investigator of the ARC Centre of Excellence for Robotic Vision (concluded in 2020). My research focuses on enabling the reliable deployment of computer vision and machine learning on mobile robots in real-world environments. I have extensive experience in applied robotic vision research resulting from my work on exciting projects such as AGRobotic detection of weed in farms using deep learning, vision-enabled autonomous underwater vehicles (AUV) to protect the Great Barrier Reef from Crown-of-Thorns Starfish and vision-based infrastructure inspection using unmanned aerial vehicles (UAV). I’ve also lectured in advanced robotics topics for undergraduates, where I taught Bayesian approaches to robot localisation, mapping, and Simultaneous Localisation and Mapping (SLAM).
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Appointments
Date Position Institution name 2022 - ongoing Senior Lecturer University of Adelaide 2022 - ongoing Adjunct Senior Lecturer Queensland University of Technology 2019 - 2022 Senior Lecturer Queensland University of Technology 2016 - 2019 Centre Research Fellow Center for Excellence in Robotic Robotics Vision (ACRV) 2012 - 2016 Postdoctoral Research Fellow Queensland University of Technology -
Research Interests
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Journals
Year Citation 2023 Wilson, S., Fischer, T., Sunderhauf, N., & Dayoub, F. (2023). Hyperdimensional Feature Fusion for Out-of-Distribution Detection. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).
2022 Hall, D., Talbot, B., Bista, S. R., Zhang, H., Smith, R., Dayoub, F., & Sünderhauf, N. (2022). BenchBot environments for active robotics (BEAR): Simulated data for active scene understanding research. International Journal of Robotics Research, 41(3), 259-269.
Scopus1 WoS12022 Rahman, Q. M., Sunderhauf, N., Corke, P., & Dayoub, F. (2022). FSNet: A Failure Detection Framework for Semantic Segmentation. IEEE Robotics and Automation Letters, 7(2), 3030-3037.
Scopus1 WoS12022 Miller, D., Sunderhauf, N., Milford, M., & Dayoub, F. (2022). Uncertainty for identifying open-set errors in visual object detection. IEEE Robotics and Automation Letters, 7(1), 215-222.
Scopus5 WoS42021 Talbot, B., Dayoub, F., Corke, P., & Wyeth, G. (2021). Robot navigation in unseen spaces using an abstract map. IEEE Transactions on Cognitive and Developmental Systems, 13(4), 791-805.
Scopus6 WoS62021 Rahman, Q. M., Corke, P., & Dayoub, F. (2021). Run-time monitoring of machine learning for robotic perception: a survey of emerging trends. IEEE Access, 9, 20067-20075.
Scopus12 WoS102019 Sünderhauf, N., Dayoub, F., Hall, D., Skinner, J., Zhang, H., Carneiro, G., & Corke, P. (2019). A probabilistic challenge for object detection. Nature Machine Intelligence, 1(9), 443.
WoS12018 Ahn, H. S., Sa, I., & Dayoub, F. (2018). Introduction to the Special Issue on Precision Agricultural Robotics and Autonomous Farming Technologies. IEEE Robotics and Automation Letters, 3(4), 4435-4438.
Scopus1 WoS12018 Hall, D., Dayoub, F., Perez, T., & McCool, C. (2018). A rapidly deployable classification system using visual data for the application of precision weed management. Computers and Electronics in Agriculture, 148, 107-120.
Scopus17 WoS102017 Bawden, O., Kulk, J., Russell, R., McCool, C., English, A., Dayoub, F., . . . Perez, T. (2017). Robot for weed species plant-specific management. Journal of Field Robotics, 34(6), 1179-1199.
Scopus73 WoS612017 Sa, I., Lehnert, C., English, A., McCool, C., Dayoub, F., Upcroft, B., & Perez, T. (2017). Peduncle Detection of Sweet Pepper for Autonomous Crop Harvesting-Combined Color and 3-D Information. IEEE Robotics and Automation Letters, 2(2), 765-772.
Scopus63 WoS532016 Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., & McCool, C. (2016). Deepfruits: A fruit detection system using deep neural networks. Sensors (Switzerland), 16(8), 1-23.
Scopus638 WoS477 Europe PMC852015 Dayoub, F., Morris, T., & Corke, P. (2015). Rubbing shoulders with mobile service robots. IEEE Access, 3, 333-342.
Scopus8 WoS82011 Dayoub, F., Cielniak, G., & Duckett, T. (2011). Long-term experiments with an adaptive spherical view representation for navigation in changing environments. Robotics and Autonomous Systems, 59(5), 285-295.
Scopus45 WoS36- Wilson, S., Fischer, T., Dayoub, F., Miller, D., & Sünderhauf, N. (n.d.). SAFE: Sensitivity-Aware Features for Out-of-Distribution Object
Detection.- Chapman, N. H., Dayoub, F., Browne, W., & Lehnert, C. (n.d.). Predicting Class Distribution Shift for Reliable Domain Adaptive Object
Detection.- Haviland, J., Dayoub, F., & Corke, P. (n.d.). Control of the Final-Phase of Closed-Loop Visual Grasping using
Image-Based Visual Servoing.- Skinner, J., Hall, D., Zhang, H., Dayoub, F., & Sünderhauf, N. (n.d.). The Probabilistic Object Detection Challenge. - Arain, B., Dayoub, F., Rigby, P., & Dunbabin, M. (n.d.). Close-Proximity Underwater Terrain Mapping Using Learning-based Coarse
Range Estimation. -
Books
Year Citation 2020 Garg, S., Sünderhauf, N., Dayoub, F., Morrison, D., Cosgun, A., Carneiro, G., . . . Milford, M. (2020). Semantics for Robotic Mapping, Perception and Interaction: A Survey (Vol. 8). United States: Now Publishers.
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Book Chapters
Year Citation 2017 Perez, T., Bawden, O., Kulk, J., Russell, R., McCool, C., English, A., & Dayoub, F. (2017). Overview of mechatronic design for a weed-management robotic system. In D. Zhang, & B. Wei (Eds.), Robotics and Mechatronics for Agriculture (1st ed., pp. 23-49). Boca Raton, USA: CRC Press.
2015 Dayoub, F., Cielniak, G., & Duckett, T. (2015). Eight weeks of episodic visual navigation inside a non-stationary environment using adaptive spherical views. In L. Mejias, P. Corke, & J. Roberts (Eds.), Springer Tracts in Advanced Robotics (Vol. 105, pp. 379-392). SPRINGER-VERLAG BERLIN.
Scopus2 WoS22011 Dayoub, F., Cielniak, G., & Duckett, T. (2011). Long-term experiment using an adaptive appearance-based map for visual navigation by mobile robots. In Towards Autonomous Robotic Systems (Vol. 6856 LNAI, pp. 400-401). Springer Berlin Heidelberg.
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Conference Papers
Year Citation 2022 Corke, P., Dayoub, F., Hall, D., Skinner, J., & Sünderhauf, N. (2022). What Can Robotics Research Learn from Computer Vision Research?. In Proceedings of the 19th International Symposium of Robotic Research (ISRR 2019), as published in Springer Proceedings in Advanced Robotics Vol. 20 (pp. 987-1003). Cham, Switzerland: Springer.
2021 Rahman, Q. M., Sünderhauf, N., & Dayoub, F. (2021). Online Monitoring of Object Detection Performance During Deployment. In Proceedings of the 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021) (pp. 4839-4845). online: IEEE.
2021 Miller, D., Sunderhauf, N., Milford, M., & Dayoub, F. (2021). Class anchor clustering: A loss for distance-based open set recognition. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV 2021) (pp. 3569-3577). online: IEEE.
Scopus21 WoS202021 Moskvyak, O., Maire, F., Dayoub, F., & Baktashmotlagh, M. (2021). Keypoint-aligned embeddings for image retrieval and re-identification. In Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021 (pp. 676-685). online: IEEE.
Scopus5 WoS62021 Zhang, H., Wang, Y., Dayoub, F., & Sünderhauf, N. (2021). VarifocalNet: An IoU-aware Dense Object Detector. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 8510-8519). online: IEEE.
Scopus121 WoS972021 Rahman, Q. M., Sunderhauf, N., & Dayoub, F. (2021). Per-frame mAP Prediction for Continuous Performance Monitoring of Object Detection during Deployment. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW 2021) (pp. 152-160). online: IEEE.
Scopus3 WoS22021 Moskvyak, O., Maire, F., Dayoub, F., Armstrong, A. O., & Baktashmotlagh, M. (2021). Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant Embeddings. In DICTA 2021 - 2021 International Conference on Digital Image Computing: Techniques and Applications (pp. 1-8). online: IEEE.
Scopus32021 Bista, S. R., Hall, D., Talbot, B., Zhang, H., Dayoub, F., & Sünderhauf, N. (2021). Evaluating the Impact of Semantic Segmentation and Pose Estimation on Dense Semantic SLAM. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 5328-5335). online: IEEE.
Scopus22020 Moskvyak, O., Maire, F., Dayoub, F., & Baktashmotlagh, M. (2020). Learning Landmark Guided Embeddings for Animal Re-identification. In 2020 IEEE Winter Applications of Computer Vision Workshops (WACVW) (pp. 12-19). online: IEEE.
Scopus5 WoS52020 Hall, D., Dayoub, F., Skinner, J., Zhang, H., Miller, D., Corke, P., . . . Sunderhauf, N. (2020). Probabilistic object detection: Definition and evaluation. In Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020 (pp. 1020-1029). online: IEEE.
Scopus36 WoS252019 Halodova, L., Dvorrakova, E., Majer, F., Vintr, T., Mozos, O. M., Dayoub, F., & Krajnik, T. (2019). Predictive and adaptive maps for long-term visual navigation in changing environments. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 7033-7039). Macau, China: IEEE.
Scopus12 WoS102019 Miller, D., Dayoub, F., Milford, M., & Sunderhauf, N. (2019). Evaluating merging strategies for sampling-based uncertainty techniques in object detection. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2019) Vol. 2019-May (pp. 2348-2354). online: IEEE.
Scopus37 WoS272019 Rahman, Q. M., Sunderhauf, N., & Dayoub, F. (2019). Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3748-3753). online: IEEE.
Scopus92019 Miller, D., Sünderhauf, N., Zhang, H., Hall, D., & Dayoub, F. (2019). Benchmarking sampling-based probabilistic object detectors. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Vol. 2019-June (pp. 42-45).
Scopus72018 Abbas, A., Maire, F., Shirazi, S., Dayoub, F., & Eich, M. (2018). A dynamic planner for object assembly tasks based on learning the spatial relationships of its parts from a single demonstration. In T. Mitrovic, B. Xue, & X. Li (Eds.), Proceedings of AI 2018: Advanced in Artificial Intelligence 31st Australasian Joint Conference Vol. 11320 LNAI (pp. 759-765). Wellington, New Zealand: Springer International Publishing.
Scopus12018 McFadyen, A., Dayoub, F., Martin, S., Ford, J., & Corke, P. (2018). Assisted Control for Semi-Autonomous Power Infrastructure Inspection Using Aerial Vehicles. In IEEE International Conference on Intelligent Robots and Systems (pp. 5719-5726). online: IEEE.
Scopus2 WoS12018 Miller, D., Nicholson, L., Dayoub, F., & Sunderhauf, N. (2018). Dropout Sampling for Robust Object Detection in Open-Set Conditions. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2018) (pp. 3243-3249). Brisbane, Australia: IEEE.
Scopus81 WoS612018 Abbas, A., Maire, F., Dayoub, F., & Shirazi, S. (2018). Combining learning from demonstration and search algorithm for dynamic goal-directed assembly task planning. In ACRA 2018 Proceedings Vol. 2018-December. Online: Australian Robotics and Automation Association. 2017 Dayoub, F., Sunderhauf, N., & Corke, P. I. (2017). Episode-Based Active Learning with Bayesian Neural Networks. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2017) Vol. 2017-July (pp. 498-500). Honolulu, Hawaii, USA: IEEE.
Scopus4 WoS22017 Hall, D., Dayoub, F., Kulk, J., & McCool, C. (2017). Towards unsupervised weed scouting for agricultural robotics. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 5223-5230). online: IEEE.
Scopus192017 Hall, D., Dayoub, F., Perez, T., & McCool, C. (2017). A transplantable system for weed classification by agricultural robotics. In 2017 IEEE International Conference on Intelligent Robots and Systems Vol. 2017-September (pp. 5174-5179). Vancouver, BC, Canada: IEEE.
Scopus4 WoS42016 Sunderhauf, N., Dayoub, F., McMahon, S., Talbot, B., Schulz, R., Corke, P., . . . Milford, M. (2016). Place categorization and semantic mapping on a mobile robot. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 2016-June (pp. 5729-5736). online: IEEE.
Scopus922016 McCool, C., Sa, I., Dayoub, F., Lehnert, C., Perez, T., & Upcroft, B. (2016). Visual detection of occluded crop: For automated harvesting. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 2016-June (pp. 2506-2512). Stockholm, Sweden: IEEE.
Scopus48 WoS352016 Talbot, B., Lam, O., Schulz, R., Dayoub, F., Upcroft, B., & Wyeth, G. (2016). Find my office: Navigating real space from semantic descriptions. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 2016-June (pp. 5782-5787). online: IEEE.
Scopus16 WoS122015 Schulz, R., Talbot, B., Lam, O., Dayoub, F., Corke, P., Upcroft, B., & Wyeth, G. (2015). Robot navigation using human cues: A robot navigation system for symbolic goal-directed exploration. In Proceedings - IEEE International Conference on Robotics and Automation Vol. 2015-June (pp. 1100-1105). Seattle, WA: IEEE COMPUTER SOC.
Scopus19 WoS112015 Dayoub, F., Dunbabin, M., & Corke, P. (2015). Robotic detection and tracking of Crown-of-Thorns starfish. In IEEE International Conference on Intelligent Robots and Systems Vol. 2015-December (pp. 1921-1928). Hamburg, GERMANY: IEEE.
Scopus21 WoS172015 Sünderhauf, N., Shirazi, S., Jacobson, A., Dayoub, F., Pepperell, E., Upcroft, B., & Milford, M. (2015). Place recognition with convnet landmarks: Viewpoint-robust, condition-robust, training-free. In Proceedings of the Robotics: Science and Systems XI Conference (RSS 2015) Vol. 11 (pp. 1-10). online: Robotics: Science and Systems Foundation.
Scopus269 WoS1952015 Hall, D., McCool, C., Dayoub, F., Sünderhauf, N., & Upcroft, B. (2015). Evaluation of features for leaf classification in challenging conditions. In Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015 (pp. 797-804). Waikoloa, HI: IEEE.
Scopus91 WoS622015 Sünderhauf, N., Shirazi, S., Dayoub, F., Upcroft, B., & Milford, M. (2015). On the performance of ConvNet features for place recognition. In IEEE International Conference on Intelligent Robots and Systems Vol. 2015-December (pp. 4297-4304). Hamburg, GERMANY: IEEE.
Scopus364 WoS3022015 Lam, O., Dayoub, F., Schulz, R., & Corke, P. (2015). Automated topometric graph generation from floor plan analysis. In Australasian Conference on Robotics and Automation, ACRA.
Scopus62014 Lam, O., Dayoub, F., Schulz, R., & Corke, P. (2014). Text recognition approaches for indoor robotics: A comparison. In Australasian Conference on Robotics and Automation, ACRA Vol. 02-04-December-2014.
Scopus42014 Morris, T., Dayoub, F., Corke, P., & Upcroft, B. (2014). Simultaneous localization and planning on multiple map hypotheses. In IEEE International Conference on Intelligent Robots and Systems (pp. 4531-4536). Chicago, IL: IEEE.
Scopus3 WoS32014 Morris, T., Dayoub, F., Corke, P., Wyeth, G., & Upcroft, B. (2014). Multiple map hypotheses for planning and navigating in non-stationary environments. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 2765-2770). Hong Kong, PEOPLES R CHINA: IEEE.
Scopus16 WoS122013 Dayoub, F., Morris, T., Upcroft, B., & Corke, P. (2013). Vision-only autonomous navigation using topometric maps. In IEEE International Conference on Intelligent Robots and Systems (pp. 1923-1929). IEEE.
Scopus312013 Dayoub, F., Morris, T., Upcroft, B., & Corke, P. (2013). One Robot, eight hours, and twenty four thousand people. In Australasian Conference on Robotics and Automation, ACRA.
Scopus12010 Dayoub, F., Duckett, T., & Cielniak, G. (2010). Short- and long-term adaptation of visual place memories for mobile robots. In Proceedings of the International Symposium on Remembering Who We Are - Human Memory for Artificial Agents - A Symposium at the AISB 2010 Convention (pp. 21-26).
Scopus22008 Dayoub, F., & Duckett, T. (2008). An adaptive appearance-based map for long-term topological localization of mobile robots. In R. Chatila, A. Kelly, & J. P. Merlet (Eds.), 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS (pp. 3364-3369). Nice, FRANCE: IEEE.
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Current Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 2023 Principal Supervisor Machine learning-enabled satellites for agile space operations Doctor of Philosophy Doctorate Full Time Mr Harrison Edward Bennett 2023 Principal Supervisor Semi-supervised object detection for mobile robots Master of Philosophy Master Full Time Mr Xiangyu Shi 2022 Co-Supervisor Enhancing Mars rovers using AI-enabled robotic vision Doctor of Philosophy Doctorate Full Time Mr Lachlan William Holden 2022 Co-Supervisor AMRS: Attention-aware Mixed Reality System Master of Philosophy Master Full Time Mr Renjie Wu 2022 Co-Supervisor Efficient Deep Learning on the Edge Doctor of Philosophy Doctorate Full Time Mr Xu Zhan
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