Deep Learning for Effective Gender Classification of Tasmania Giant Crabs

The giant crab fishery in southeast Australia currently suffers from a lack of accurate size and sex data to establish population dynamics essential for the management of the industry. Determining these traits manually on boats or from observers using video would be time-consuming and prone to observer error. This research aims to find an efficient, accurate, and fast way to identify giant crabs' gender to eliminate the manual cost and operation time. This problem can be solved by artificial intelligence technologies, particularly Convolutional Neural Networks (CNN). However, CNNs can detect the crabs but find it challenging to identify their genders from the top view (carapace). Other issues include the lack of training data and the demand for compact system to be deployed on boat easily. With such constraints of effectiveness and efficiency, we address the problem of crab gender classification by proposing a cascading architecture. First, we simplify a light-weight object detection model (MobileNet) for carapace localisation. After that our model extract the carapace area on crab images for classification modelling. According to our experiments' results, with the use of light-weight CNNs for our cascading architecture, we achieved the highest accuracy of up to 96.34% with an efficiency of around 2 frames per second in a Raspberry Pi V4.