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.
Highlights
Due to the global population growth, the average consumption of aquaculture products has been increasing rapidly for the last two decades
Based on our previous work [3] employing deep learning Faster- rcnn [4] as an object detector to implement the tracking task for measuring the moving speed of fish, this paper presents a real-time solution for the problem of detecting anomalous behaviors for underwater fish
The proposed approach is characterized in that information provided by the deep learning-based object detectors is utilized throughout the subsequent steps of directed cycle graph (DCG), tracking, and dynamic time warping (DTW)
Summary
Due to the global population growth, the average consumption of aquaculture products has been increasing rapidly for the last two decades. Vast benefits of monitoring the health status of cultivated fish can be obtained from using AIoT, as aquaculture industry inevitably involves breeding and selection of brood fish. Based on our previous work [3] employing deep learning Faster- rcnn [4] as an object detector to implement the tracking task for measuring the moving speed of fish, this paper presents a real-time solution for the problem of detecting anomalous behaviors for underwater fish. Real-time detection of anomalous behaviors can be applied to aquaculture farms and sea cages to help prevent diseases and sudden death, so as to reduce financial loss.
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