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 an academic and researcher specializing in the intersection area of computer vision, machine learning, and robotics. Currently, I serve as a Senior Lecturer with the School of Computer Science and the Australian Institute for Machine Learning (AIML) at the University of Adelaide. Concurrently, I hold an Adjunct position at the Faculty of Engineering at Queensland University of Technology (QUT) and an Associate Investigator with QUT Centre for Robotics (QCR). I served as a Chief Investigator of the ARC Centre of Excellence for Robotic Vision. My research focuses on enabling the reliable deployment of computer vision and machine learning on mobile robots in real-world environments. I gained a wealth of expertise in applied robotic vision research by participating in various exciting projects, ranging from leveraging machine learning for agricultural innovation and environmental conservation to advancing infrastructure monitoring through vision-based autonomous systems. As an educator for undergraduate students, I specialize in topics such as programming, computer vision, machine learning, and robotic perception.
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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 Pershouse, D., Dayoub, F., Miller, D., & Sünderhauf, N. (2023). Addressing the Challenges of Open-World Object Detection. 2023 Shi, X., Qiao, Y., Wu, Q., Liu, L., & Dayoub, F. (2023). Improving Online Source-free Domain Adaptation for Object Detection by
Unsupervised Data Acquisition.2023 Chapman, N. H., Dayoub, F., Browne, W., & Lehnert, C. (2023). Predicting Class Distribution Shift for Reliable Domain Adaptive Object Detection. IEEE Robotics and Automation Letters, 8(8), 1-8.
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.
Scopus2 WoS22022 Wilson, S., Fischer, T., Dayoub, F., Miller, D., & Sünderhauf, N. (2022). SAFE: Sensitivity-Aware Features for Out-of-Distribution Object
Detection. IEEE International Conference on Computer Vision 2023.2022 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), 1-8.
Scopus6 WoS52022 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.
Scopus19 WoS82021 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.
Scopus11 WoS72021 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.
Scopus27 WoS162020 Haviland, J., Dayoub, F., & Corke, P. (2020). Control of the Final-Phase of Closed-Loop Visual Grasping using
Image-Based Visual Servoing.2020 Arain, B., Dayoub, F., Rigby, P., & Dunbabin, M. (2020). Close-Proximity Underwater Terrain Mapping Using Learning-based Coarse
Range Estimation.2019 Skinner, J., Hall, D., Zhang, H., Dayoub, F., & Sünderhauf, N. (2019). The Probabilistic Object Detection Challenge. 2019 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.
Scopus4 WoS22018 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.
Scopus18 WoS112017 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.
Scopus109 WoS752017 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.
Scopus81 WoS592016 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.
Scopus792 WoS539 Europe PMC1112015 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.
Scopus48 WoS37- Clement, B., Dubromel, M., Santos, P. E., Sammut, K., Oppert, M., & Dayoub, F. (n.d.). Hybrid Navigation Acceptability and Safety. Proceedings of the AAAI Symposium Series, 2(1), 11-17.
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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 2024 Wu, R., Wang, H., Dayoub, F., & Chen, H. T. (2024). Segment beyond View: Handling Partially Missing Modality for Audio-Visual Semantic Segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 38 (pp. 6100-6108). Association for the Advancement of Artificial Intelligence (AAAI).
2023 Wilson, S., Fischer, T., Dayoub, F., Miller, D., & Sünderhauf, N. (2023). SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 12 pages). Online: IEEE.
Scopus12023 Wilson, S., Fischer, T., Sunderhauf, N., & Dayoub, F. (2023). Hyperdimensional Feature Fusion for Out-of-Distribution Detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2023) (pp. 2643-2653). Online: IEEE.
Scopus52022 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 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.
Scopus10 WoS12021 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.
Scopus7 WoS62021 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.
Scopus59 WoS312021 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.
Scopus16 WoS92021 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.
Scopus420 WoS2122021 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.
Scopus11 WoS72021 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.
Scopus5 WoS32021 Moskvyak, O., Maire, F., Dayoub, F., & Baktashmotlagh, M. (2021). SEMI-SUPERVISED KEYPOINT LOCALIZATION. In ICLR 2021 - 9th International Conference on Learning Representations (pp. 1-11). Virtual only: Open Review.
Scopus112020 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.
Scopus8 WoS82020 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.
Scopus67 WoS382019 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.
Scopus18 WoS132019 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.
Scopus66 WoS382019 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.
Scopus15 WoS122019 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).
Scopus102018 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.
Scopus22018 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.
Scopus3 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.
Scopus143 WoS752018 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 WoS32017 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.
Scopus252017 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.
Scopus104 WoS752016 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.
Scopus54 WoS382016 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.
Scopus18 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.
Scopus23 WoS122015 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.
Scopus27 WoS192015 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.
Scopus304 WoS1912015 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). HI, Waikoloa: IEEE.
Scopus99 WoS692015 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.
Scopus416 WoS3272015 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.
Scopus102014 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 N. Amato (Ed.), IEEE International Conference on Intelligent Robots and Systems (pp. 1923-1929). JAPAN, Tokyo: IEEE.
Scopus32 WoS212013 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.
Scopus74 WoS48 -
Preprint
Year Citation 2024 Mallick, P., Dayoub, F., & Sherrah, J. (2024). Wasserstein Distance-based Expansion of Low-Density Latent Regions for
Unknown Class Detection.
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Current Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 2024 Principal Supervisor Foundation models for goal-oriented reinforcement learning and intrinsic exploration Master of Philosophy Master Full Time Mr Dustin Wyly Craggs 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 2023 Principal Supervisor 3D Scene Understanding and Change Tracking Doctor of Philosophy Doctorate Full Time Mr Chun-Jung Lin 2023 Principal Supervisor Embodied AI: Navigating and Decoding Complexity Doctor of Philosophy Doctorate Full Time Harrison Alexander Inglis 2023 Co-Supervisor 3D indoor Scene Reconstruction Doctor of Philosophy Doctorate Full Time Mr Wenbo Zhang 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
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