Recognition and localization of freshwater fish heads and tails based on lightweight neural networks

Abstract

ABSTRACT The quality of fish pre-treatment processing directly affected the production competitiveness of the fish industry. The removal of heads and tails is one of the key technologies in the fish processing. This study proposed an identification method of fish head and tail, and the original YOLOV3 model was improved by replacing the backbone feature extraction network of the YOLOV3 model with the lightweight neural network MobileNetv3. Firstly, a freshwater fish image dataset was created and divided into the training, validation and test sets with the assigned ratio of 6:2:2. Next, the freshwater fish dataset was trained using the target detector YOLOV3. Finally, the average accuracy mAP (mean of Average Precision) and the average image detection time were used as the accuracy and speed indexes to evaluate the detection effect of the model. In addition, the SSD-MobileNetv3 and SSD-VGG16 were introduced into present study and they were compared with the improved algorithm. The experimental results showed that the detection speed of the YOLOV3 model with MobileNetV3 was significantly improved. The mAP of YOLOV3-MobileNetv3 model was 98.36%, the inference speed was 28.2 ms, which was 5.09%, 4.24% and 2.07% higher than the mAP of other three models (SSD-VGG16, SSD-MobileNetv3 and YOLOV3-Darknet-53), and the average detection time shortened by 86%, 9.99% and 29%, respectively. Therefore, this experimental method of head and tail of freshwater fish could achieve real-time detection and recognition of various kinds of freshwater fish, which had the great advantages of high detection accuracy and fast detection speed.