Detection method of high value marine food organisms based on lightweight deep learning Mobilenet-SSD network
YU Weicong1, GUO Xianjiu1,2*, LIU Yufa1, LIU Ting1, LI Yawei1
1.College of Information Engineering, Dalian Ocean University, Dalian 116023, China; 2.Key Laboratory of Marine Information Technology of Liaoning Province, Dalian 116023, China
Abstract: In order to accurately understanding of the culture distribution of high value sea food organisms in water and get rid of the traditional way of relying on diving to probe into the situation of marine sea organisms, a lightweight deep learning Mobilenet-SSD network based detection method of high value sea food organisms was proposed via collecting real-time images of marine organisms through underwater cameras under fishing boats and quickly detect target marine organisms. The self-built data set was trained on Mobilenet-SSD network to realize the accurate recognition of 3 types of high value marine food organisms, with recognition rate of 81.43% in sea urchin, 86.02% in sea cucumber and 89.44% in scallop, and average accuracy of 85.79% in the test. The comparison of Mobilenet-SSD with Tiny-YOLO network and VGG-SSD network on the same device, repectively, indicated that Mobilenet-SSD in the case of no loss of accuracy had more both accuracy and real-time than Tiny-YOLO network did, with saving 80% of time compared with VGG-SSD network.
俞伟聪, 郭显久, 刘钰发, 刘婷, 李雅薇. 基于轻量化深度学习Mobilenet-SSD网络模型的海珍品检测方法[J]. 大连海洋大学学报, 2021, 36(2): 340-346.
YU Weicong, GUO Xianjiu, LIU Yufa, LIU Ting, LI Yawei. Detection method of high value marine food organisms based on lightweight deep learning Mobilenet-SSD network. Journal of Dalian Ocean University, 2021, 36(2): 340-346.
2021, Vol. 36 
