Fish Detection and Classification for Automatic Sorting System with an Optimized YOLO Algorithm

Abstract

Automatic fish recognition using deep learning and computer or machine vision is a key part of making the fish industry more productive through automation. An automatic sorting system will help to tackle the challenges of increasing food demand and the threat of food scarcity in the future due to the continuing growth of the world population and the impact of global warming and climate change. As far as the authors know, there has been no published work so far to detect and classify moving fish for the fish culture industry, especially for automatic sorting purposes based on the fish species using deep learning and machine vision. This paper proposes an approach based on the recognition algorithm YOLOv4, optimized with a unique labeling technique. The proposed method was tested with videos of real fish running on a conveyor, which were put randomly in position and order at a speed of 505.08 m/h and could obtain an accuracy of 98.15%. This study with a simple but effective method is expected to be a guide for automatically detecting, classifying, and sorting fish.

Fusion of Deep Sort and Yolov5 for Effective Vehicle Detection and Tracking Scheme in Real-Time Traffic Management Sustainable System

“…The object detection approach is categorized into two methods: the two-stage and one-stage method. The two-stage approach based on old research gives high prediction accuracy but cannot achieve speed and higher performance [5]. The two-stage category networks to detect objects are Region-Based Convolutional Neural Network (R-CNN), Fast R-CNN, Faster R-CNN, and Spatial Pyramid Pooling Network (SPP-net) [23].…”

“…Various object detection algorithms have been proposed based on the one-stage approach. They are fast and make predictions of objects with high efficiency [5]. Joseph Redmon et al [29] proposed a YOLO model in 2016 that is state-of-the-art in real-time object detection.…”

“…With the rapid growth in the global economy, the living standard has improved [2][3][4]. With the rapid growth of vehicles on the roadways, there are some significant causes of traffic safety issues such as vehicle accidents, drunk driving, road conditions, and traffic violations [5]. Road traffic congestion in urban areas is the most common issue due to the density of traffic and the growth of the urban population [6].…”

Fully documented fisheries : Final report

“…Large amounts of collected data require automated processing to efficiently extract the task-specific information. Several studies [11,18,19,12,9,10,20] demonstrated the application D R A F T -w o r k i n p r o g r e s s of well-established deep neural networks, such as Single Shot MultiBox Detector [11], Region-Based Convolutional Neural Networks [18,19], and most of them used methods from the YOLO-family approaches [12,9,10,20,19,13,19,21,14]. In contrast, there are few studies dealing with fish detection along with weight estimation.…”