|
|
|
|
|
Siyi Zhou, Kewei Cai, Yanhong Feng, Xiaomeng Tang, Hongshuai Pang, Jiaqi He and Xiang Shi
In aquaculture, the accurate recognition of fish underwater has outstanding academic value and economic benefits for scientifically guiding aquaculture production, which assists in the analysis of aquaculture programs and studies of fish behavior. Howeve...
ver más
|
|
|
|
|
|
|
|
|
|
|
Jinnan Wang, Weiqin Tong and Xiaoli Zhi
Convolutional neural networks (CNNs) have made impressive achievements in image classification and object detection. For hardware with limited resources, it is not easy to achieve CNN inference with a large number of parameters without external storage. ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yule Chen, Hong Liang and Shuo Pang
Underwater target classification methods based on deep learning suffer from obvious model overfitting and low recognition accuracy in the case of small samples and complex underwater environments. This paper proposes a novel classification network (Effic...
ver más
|
|
|
|
|
|
|
|
|
|
|
Guocheng Liu, Caixia Zhang, Qingyang Xu, Ruoshi Cheng, Yong Song, Xianfeng Yuan and Jie Sun
In view of difficulty in application of optical flow based human action recognition due to large amount of calculation, a human action recognition algorithm I3D-shufflenet model is proposed combining the advantages of I3D neural network and lightweight m...
ver más
|
|
|
|
|
|