Accurate Fish Detection under Marine Background Noise Based on the Retinex Enhancement Algorithm and CNN

• Main Authors: Chen, Y. (Author), Ling, Y. (Author), Zhang, L. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: convolutional neural network; deep learning; fish detection; image preprocessing
• Online Access: View Fulltext in Publisher

 Underwater detection equipment with fish detection technology has broad application prospects in marine fishery resources exploration and conservation. In this paper, we establish a multiscale retinex enhancement algorithm and a multi-scale feature-based fish detection model to improve underwater detection accuracy and ensure real-time performance. During image preprocessing, the enhancement algorithm combines the bionic structure of the fish retina and classical retinex theory to filter out underwater environmental noise. The detection model focuses on improving the detection performance on small-size targets using a deep learning method based on a convolutional neural network. We compare our method to current mainstream detection models (Faster R-CNN, RetinaNet, YOLO, SSDetc.), and the proposed model achieves better performance, with a mean Average Precision (mAP) of 78.31% and a mean Miss Rate (mMR) of 54.11% in the open fish image data set. The test results for the data from the field experiment prove the feasibility and stability of our model. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.