Intelligent Automation Research Group

The Intelligent Automation Research Group is focused on the development and deployment of smart automation systems for inspection and monitoring in a wide range of industrial applications.

Intelligent automation is an emerging stream of the fourth industrial revolution. The emergence of ubiquitous computing and cheap sensing devices have enabled the automation to escape the shopfloor and become an important element of contemporary living.

In terms of applications, the Group has a number of substantial projects with different industries for the development of intelligent automation systems. For instance, the aim of the Group’s navigation project is to build the foundations required to develop intelligent safety warning systems for mobile industrial platforms.

In terms of intelligent inspection and monitoring applications, the Group is also working with food and construction industries to deploy intelligent automation solutions.


Key research areas

  • Computer vision
  • Statistical information fusion
  • Robotics
  • Machine learning
  • Autonomous systems


Key research projects

Information Fusion for Tracking Objects in Large-Scale Sensor Network

This project aims to develop a mathematical framework to combine multi-modal information coming from multiple sensors. These mobile sensors will be spatially distributed over a large-scale area for the purpose of multi-object tracking. The main application of this framework is for cooperative perception for intelligent decision making. Expected outcomes include a novel technique to integrate receiving information from multiple mobile agents (e.g. vehicle) to enhance their ability to anticipate situations in dynamic environments and to act effectively to enhance safety. This should provide benefits for the development of cooperative autonomous driving to enhance road safety.

Amirali K. Gostar (PRJ00000120), ARC DECRA Project, 2021-2024, $ $403K

Automated Integrity Assessment of Self-Piercing Rivet Joints: i4.0 Approach

Lightweighting in the car industry by the use of aluminium reduces emissions substantially. It entails joining the car body sections by self-piercing rivets rather than the traditional spot welds. We aim to fill the technology gap for effective quality control of these joints. The project expects to solve the problem by merging industry 4.0 principles, three-dimensional X-ray technology, machine learning computer vision and structural mechanics. The expected outcomes are technologies for automation-friendly assessment of these joints. This should benefit industries from medical to electronics to automatically spot a random and delicate abnormality within a solid of complex geometry, such as that in live tissue or an electronic circuit.

Bab-Hadiasahar, Tennakoon, Easton, Hoseinnezhad, Mouritz, (LP 190100165), ARC Linkage Project with Ford Motor Company (USA), 2020-2023, $500K.

Visual intelligence for safe vehicle operation in industrial environments

The aim of this project is to develop intelligent safety devices for vehicles in loosely constrained industrial settings (like fruit and vegetable markets, construction sites, etc). The rate of accidents in such environments is high due to heavy vehicles, increased demand of urgency and negligence on part of pedestrians and drivers. We focus on a vision-based collision avoidance technology to design human assisting automated safety systems.
Bab-Hadiasahar, Suter, Hoseinnezhad, Neugebauer (LP160100662), ARC Linkage Project, 2016-2019, $356K.

Crowd tracking and visual analytics for rapidly deployable imaging devices

The project aims to develop visual analytics and machine intelligence technology for commercial time-lapse imaging platforms. The focus is to use embedded systems with the imaging platforms to introduce programmability to the time-lapse cameras. This will further be extended to develop an intelligent rapidly deployable imaging product capable of tracking crowds and particular behaviours at events.
Hoseinnezhad, Vo, Bab-Hadiasahar, Accadia (LP 160101081), ARC Linkage Project, 2016-2019 $302K.

Submarine dynamics and control

The objective of this project is to help the Maritime Division of Science and Technology (DST) Group in investigating the manoeuvring, control and propulsion hydrodynamics of submarines. This project involves studies in computational and experimental fluid dynamics, acoustics, mathematical modelling and control theory to characterise various dynamics and requirements of submarine control.
Bab-Hadiasahar, Rao, DST Group, $2.2M, 2016-2022.

Intelligent solutions for boxed beef trim export enhancement

Australia’s boxed beef export faces many challenges which cause hindrance to increasing export. These are mainly due to manual labelling of boxes and manual integrity inspection at the abattoirs. This project aims to review both these issues and develop solutions which may help increase export of boxed beef trims.
Bab-Hadiasahar, Hoseinnezhad, AMPC, $280K, 2017-2018.

Automated visual inspection and preparation of live animals for meat processing

The project aims to design an automated system capable of visual inspection and cleaning of animals for slaughter. The system will also focus on detecting animal contamination (in terms of dirt or faecal material) in lairages. The proposed cleaning station can be extended to measure animal behaviour and other characteristics.
Bab-Hadiasahar, Hoseinnezhad, Gill, AMPC, $446K, 2014-2017.


Key publications

  • W. Chuah, R. Tennakoon, R. Hoseinnezhad, A. Bab-Hadiashar and, D. Suter, ITSA: An Information-Theoretic Approach to Automatic Shortcut Avoidance and Domain Generalization in Stereo Matching Networks, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.Ammar Mansoor Kamoona, Amirali Khodadadian Gostar, Alireza Bab-Hadiashar, Reza Hoseinnezhad, Multiple instance-based video anomaly detection using deep temporal encoding–decoding, Expert Systems with Applications, 2023.
  • W. Chuah, R. Tennakoon, R. Hoseinnezhad, D. Suter and A. Bab-Hadiashar, Semantic Guided Long Range Stereo Depth Estimation for Safer Autonomous Vehicle Applications, IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 18916-18926, 2022.
  • Amirali K. Gostar, Tharindu Rathnayake, Ruwan Tennakoon,  Alireza Bab-Hadiashar, Giorgio Battistelli, Luigi Chisci, and Reza Hoseinnezhad, Centralized Cooperative Sensor Fusion for Dynamic Sensor Network With Limited Field-of-View via Labeled Multi-Bernoulli Filter," in IEEE Transactions on Signal Processing, 2021.
  • Ruwan Tennakoon, Alireza Sadri, Reza Hoseinnezhad, and Alireza Bab-Hadiashar, “Effective Sampling: Fast Segmentation Using Robust Geometric Model Fitting,” IEEE Transactions on Image Processing, Vol. 27, Issue 9, pp. 4182–4194, 2018.
  • Amirali K. Gostar, Reza Hoseinnezhad, Weifeng Liu and Alireza Bab-Hadiashar, Sensor-management for multitarget filters via minimization of posterior dispersion, IEEE Transactions on Aerospace and Electronic Systems, Volume 53, Issue 6, pp. 2877–2884, 2017.
  • Khalid Yousif, Alireza Bab-Hadiashar, Reza Hoseinnezhad, 3D SLAM in texture-less environments using rank order statistics, Robotica, Volume 35, Issue 4, pp. 809–831, 2017.
  • Ruwan Tennakoon, Alireza Bab-Hadiashar, Zhenwei Cao, Reza Hoseinnezhad, David Suter, Robust model fitting using higher than minimal subset sampling, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 38, Number 2, pp. 350–362, 2016.
  • Amir Dadashnialehi, Alireza Bab-Hadiashar, Zhenwei Cao, Ajay Kapoor 2014, "Intelligent Sensorless ABS for In-Wheel Electric Vehicles", IEEE Transactions on Industrial Electronics, vol.61, no.4, pp.1957-1969, 2014.
  • Hamid Khayyam, Alireza Bab-Hadiashar, “Adaptive intelligent energy management system of plug-in hybrid electric vehicle”, Energy, Volume 69: 319-335, 2014.
  • Alireza Bab-Hadiashar, Ruwan Tenekon, Marleen de Bruijne, “Quantification of Smoothing Requirement for 3D Optic Flow Calculation of Volumetric Images”, IEEE Transactions on Image Processing, 22(6): 2128-2137, 2013.



Group Leader

Professor Ali Bab-Hadiashar

Academic Staff

Research Fellows

  • Dr Andrew Smith
  • Dr Wei Qin Chuah
  • Dr Sharmila Kayastha
  • Dr Sargon Gabriel

Research Candidates

  • Aidan Blair
  • James Klupacs
  • Christian Milianti
  • Van Duong Phan
  • Sundaram Muthu
  • Ayman Mukhaimar
  • Mohammed Imran Hossain
  • Ching Nok To
  • Varshan Beik
  • Nida Ishtiaq
  • Xan MacAtangay
  • Steven Korevar
  • Thomas Bentham
  • Mohammad Adhinehvand
  • Muhammad Shoaib



The Intelligent Automation Research Group are actively seeking to recruit research fellows and PhD students. 



Professor Ali Bab-Hadiashar 
+61 3 9925 6192

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Acknowledgement of Country

RMIT University acknowledges the people of the Woi wurrung and Boon wurrung language groups of the eastern Kulin Nation on whose unceded lands we conduct the business of the University. RMIT University respectfully acknowledges their Ancestors and Elders, past and present. RMIT also acknowledges the Traditional Custodians and their Ancestors of the lands and waters across Australia where we conduct our business - Artwork 'Luwaytini' by Mark Cleaver, Palawa.

aboriginal flag
torres strait flag

Acknowledgement of Country

RMIT University acknowledges the people of the Woi wurrung and Boon wurrung language groups of the eastern Kulin Nation on whose unceded lands we conduct the business of the University. RMIT University respectfully acknowledges their Ancestors and Elders, past and present. RMIT also acknowledges the Traditional Custodians and their Ancestors of the lands and waters across Australia where we conduct our business.