The significance of koalas to Australia extends beyond their iconic status, with the industry surrounding them valued at $3.2 billion and providing employment for 30,000 individuals. Ever increasing pressures from urbanisation, land clearing, disease, bushfires and the spread of non-native vegetation and predators have resulted in the continual deterioration in natural koala populations. Effective monitoring and protection of koalas (and their habitat) is critical in reversing this decline. Of particular concern is how to manage koalas at the urban interface – especially with regard to koalas navigating structures such as roads and motorways. While there exist numerous types of infrastructure and strategies to theoretically allow safe passage of koalas between habitat that is divided by road networks, there is limited data on the known effectiveness of these approaches. Part of the problem lies in the ability to detect, monitor and report on the numbers of koalas in the area and their traversal behaviours. Unfortunately, strategies that rely on human observation are piecemeal, and using koala injury/fatalities statistics on roads is not an ideal approach. This paper presents a computer vision enhanced IoT koala monitoring and recognition system that can be used to detect koalas in their native surroundings non-intrusively. The cameras are deployed in places of interest near fauna road crossings. Motion sensing triggers the cameras to take several seconds of video footage that is relayed to the Cloud. Machine learning algorithms process the video footage to determine whether a koala has been spotted. Experimental results demonstrate that our best model on YOLO8 achieve 97.5 AP, 96.5 AR, 99.2 mAP@50, and 97.1 mAP@50-95 in our dataset which contains both daytime and night-time images. Relevant conservation groups and stakeholders can then use our outcomes to target their koala protection strategies accordingly. The system has now been used in multiple koala conservation initiatives. This paper outlines the system including hardware and the image processing approach, deployments for different koala management programs, challenges faced/overcome (technical, practical and logistical), and possibilities for future directions to enhance the technology.