|Title:||Anomalous Behaviors Detection for Underwater Fish Using AI Techniques||Authors:||Wang, Jung-Hua
|Keywords:||Fish;Aquaculture;Oceans;Object detection;Detectors;Real-time systems;Deep learning;Anomalous behavior analysis;deep learning;object detection;tracking;dynamic time warping;directed cycle graph||Issue Date:||Apr-2020||Publisher:||IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC||Journal Volume:||8||Start page/Pages:||224372-224382||Source:||IEEE ACCESS||Abstract:||
Anomalous events detection in real-world video scenes is a challenging problem owing to the complexity of anomaly and the untidy backgrounds and objects in the scenes. Although there are already many studies on dealing with this problem using deep neural networks, very little literature aims for real-time detection of the anomalous behavior of fish. This paper presents an underwater fish anomalous behavior detection method by combining deep learning object detection, DCG (Directed Cycle Graph), fish tracking, and DTW (Dynamic Time Warping). The method is useful for detecting the biological anomalous behavior of underwater fish in advance so that early countermeasures can be planned and executed. Also, through post-analysis it is possible to access the cause of diseases or death, so as to reduce unnecessary loss, facilitate precision breeding selection, and achieve ecological conservation education as well. A smart aquaculture system incorporating the proposed method and IoT sensors allows extensive data collection during the system operation in various farming fields, thus enabling to develop optimal culturing conditions, both are particularly useful for researchers and the aquaculture industry.
|Appears in Collections:||水產養殖學系|
14 LIFE BELOW WATER
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.