Counting Abalone with High Precision Using YOLOv3 and DeepSORT

Abstract

In this research work, an approach using You Only Look Once version three (YOLOv3)-TensorFlow for abalone detection and Deep Simple Online Real-time Tracking (DeepSORT) for abalone tracking in conveyor belt systems is proposed. The conveyor belt system works in coordination with the cameras used to detect abalones. Considering the computational effectiveness and improved detection algorithms, this proposal is promising compared to the previously proposed methods. Some of these methods have low effectiveness and accuracy, and they provide an incorrect counting rate because some of the abalones tend to entangle, resulting in counting two or more abalones as one. Conducting detection and tracking research is crucial to achieve modern solutions for small- and large-scale fishing industries that enable them to accomplish higher automation, non-invasiveness, and low cost. This study is based on the development and improvement of counting analysis tools for automation in the fishing industry. This enhances agility and generates more income without the cost created by inaccuracy.