The Department of Infrastructure, Planning and Logistics on behalf of the Department of Primary Industry and Resources (DPIR) have investigated the opportunity to upgrade the road access to the boat ramp at Stuart’s Tree Fishing Camp at Point Stuart, Northern Territory.
Point Stuart Road commences from the Arnhem Highway, approximately 100km from the Stuart Highway turnoff. The subject section is currently a narrow access track commencing at the boundary of the Mary River National Park, approximately 11.40 km north of the Shady Camp turnoff.
This project arose from a letter submitted by Terry Holtz of Stuart’s Tree Fishing Camp on 25 January 2017. In this letter, Mr Halse proposed a substantial improvement be made for public access to the waters of Chambers Bay and Finke Bay by upgrading the road access, boat launch facilities and related landside facilities at Point Stuart.
The outcome of this project is to open up access to the waters of Finke Bay and Chambers Bay, providing an alternative access point to areas such as Wildman River, Love Creek, Carmor Creek and reefs in the area, which in turn alleviates demand on the boat ramp at Shady Camp. Access to the Stuart’s Memorial Cairn (a site of substantial historical significance and the place where John McDouall Stuart reached the north coast of Australia after crossing the continent) shall also be improved.
In line with the NT Government’s $50 Million election commitment to invest in recreational fishing infrastructure, this project will encompass the upgrade of approximately 29 km of Point Stuart Road access track to public road standards which will improve road trafficability and safety of road users.
NT Fisheries have been monitoring the Greater Darwin Region reef fish protection areas for the last four years – collecting over 400 hours of video using baited remote underwater video systems (BRUVS). Processing this video, frame by frame to identify fish species, is time and labour intensive.
In collaboration with Microsoft, NT Fisheries has researched the application of computer vision and machine learning to automate the process of fish identification. A successful prototype was built to locate fish within a video frame with greater than 90% accuracy and identify key species with greater than 75% accuracy.
This is a demonstration of remarkable technology given some of these fish images appear for only a fleeting period and often in highly turbid water.
Broader applications of the technology are also being investigated for monitoring bycatch on commercial vessels and for biosecurity monitoring purposes.