Multi-detector and motion prediction-based high-speed non-intrusive fingerling counting method

Fingerling counting is a basic operation in fish farming and provides an important guideline for many aspects of aquaculture. However, most of the current counting methods are inefficient or computationally cumbersome. This study proposed a high-speed, non-intrusive fingerling counting method based on multiple detectors and a motion prediction model, which achieved high-accuracy counting under the condition of low-frame rate. Firstly, to effectively detect and locate the adherent fingerlings, the detector was accomplished by combining the mixture of Gaussian-based (MOG) segmentation algorithm and the local extremum-based blob detection algorithm. Secondly, three different functions were used to construct a motion prediction model to predict the approximate probability of the fingerlings at each position in the previous frame. Thirdly, the cost matrix was constructed with probability as the feature to associate the fingerlings in the consecutive frames, and the newly appeared fingerlings were counted in real-time, realising the continuous fingerling counting with high precision. Through testing and analysis on 52 collected datasets under low-frame-rate (10 fps) acquisition conditions using largemouth bass (Micropterus salmoides, 3–5 cm) and crucian carp (Carassius auratus, 2–6 cm), results indicated that the best motion prediction model with segmentation function reached over 99% average counting accuracy for both species, with a standard deviation of accuracy less than 0.7%. This method provides a low-cost, high-speed, and stable application solution for computer vision-based fingerling counting.