Count and location determination of Nile Tilapia (Oreochromis niloticus) using convolutional neural network and CLAHE
Paper Details
Count and location determination of Nile Tilapia (Oreochromis niloticus) using convolutional neural network and CLAHE
Abstract
Fish counting in aquaculture is an important task in fish population estimation. However, it is very challenging because of the diversity of backgrounds, uncertainty of fish motion, and obstruction between objects. To solve this problem, a model using Convolutional Neural Network (CNN) and Contrast Limited Adaptive Histogram Equalization (CLAHE) is proposed to provide an advanced and efficient counting method for aquaculture. The methodology involved image acquisition, CNN implementation, and evaluation. First, images were manually annotated from video frames. Then, a CNN was trained on the training dataset to detect the tilapia and determine its location. Lastly, the performance of the method was evaluated and compared with other assessment methods. The results show that the study gained 95%, 87%, and 91% for precision, recall, and F1-score, respectively. Further, the mean average precision at 0.5 resulted in 94.21%; thus, the study can detect and locate the fish in a tank and be integrated into a feeding management system.
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Ben Saminiano, Arnel Fajardo, Ruji Medina (2023), Count and location determination of Nile Tilapia (Oreochromis niloticus) using convolutional neural network and CLAHE; JBES, V23, N1, July, P1-6
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