Framework for fish freshness detection and rotten fish removal in Bangladesh using mask R–CNN method with robotic arm and fisheye analysis

Abstract

Fish-exporting countries must meet international standards and customer expectations to avoid trade disruption and economic repercussions. Ensuring the quality of fish products is therefore not just a matter of reputation but also of economic stability. Bangladesh, a major fish exporter, maintaining the quality of exported fish products is crucial. A single faulty product can have serious consequences, potentially causing harm. For this reason, we develop a deep learning-based approach capable of detecting and dividing rotten fish. We have used the Mask R–CNN method for our model. A device captures images of fish eyes and sends them to a computer system. The condition of a fish, whether fresh or rotten, can be determined by its eyes. A collection of 5000 image datasets is developed in this research work. The input images start matching the dataset through the Mask R–CNN and give us the result. Based on the result, if the fish is found fresh, it will proceed through the conveyor system; however, if it is identified as rotten, a robotic arm will separate it. To test its efficiency and reliability, we have tested it with 5000 images. And the test satisfied us with the results of 96.5% accuracy. With the assistance of our model, fish-exporting nations can efficiently distinguish between fresh and rotten fish, enhancing their ability to export more significant quantities of high-quality fish.

Keywords

• Fish freshness detection
• Rotten fish removal
• Mask R–CNN method
• Robotic arm
• Image processing
• Deep learning