This study attempts to explore the methods to identify fishes in still images taken from the electronic observer system installed on longline fishing vessels. Sobel filter was used to capture image features and transform it into histogram. Different recognition mechanisms were tested to find out the best method. Captured image features include: gradient magnitude, angle, and curvature. Correlation coefficient was chosen as baseline of recognition mechanisms. K-means clustering method was used; however, the results showed the effectiveness of recognition is not as expected. Thus, "fish global feature" and "fish local feature" were proposed. First, a single tuna template was created and tested. After the validity was established, by combining more templates and utilizing Bagging algorithm, the recognition accuracy can be improved. In the experiment, 96.0% fish recognition rate was obtained. The results show that adequate number of templates were created with corresponding with site conditions, the recognition accuracy can be increased.
|Number of pages||10|
|Journal||Journal of Taiwan Society of Naval Architects and Marine Engineers|
|Publication status||Published - 2016 Aug|
All Science Journal Classification (ASJC) codes
- Ocean Engineering
- Mechanical Engineering