Automatic Fish Identification Using Single Shot Detector - Arie Vatresia; Ruvita Faurina; Vivin Purnamasari; Indra Agustian (CommIT (Communication and Information Technology) Journal, 2022)
The vast sea conditions and the long coastline make Bengkulu one of the provinces with a high diversity of marine fish. Although it is predicted to have high diversity, data on the diversity of marine fish on the Bengkulu coast is still very limited, especially in the process of fish species detection. With the development and expansion of computer capabilities, the ability to classify fish can be done with the help of computer equipment. The research presents a new method of automating the detection of marine fish with a Single Shot Detector method. It is a relatively simple algorithm to detect an object with the help of a MobileNet architecture. In the research, the Single Shot Detector used is six extra convolution layers. Three of the extra layers can generate six predictions for each cell. The Single Shot Detector model, in total, can generate 8,732 predictions. The research succeeds in identifying seven from ten genera of marine fish with a total dataset of 1,000 images, with 90% training data and 10% validation data. Each fish genus has 100 images with different shooting angles and backgrounds. The results show that the Single Shot Detector model with MobileNet architecture gets an accuracy value of 52.48% for the identification of 10 genera of marine fish.