Underwater Resource Detection Using Image Processing

The growing demand for marine resources makes underwater resource detection a significant topic of research. Image processing techniques show a lot of potential in detecting and mapping underwater resources such as marine creatures, minerals, and archaeological relics. This study describes a unique method for detecting underwater resources that employs the Haar Cascade Classifier with Contrast Limiting Adaptive Histogram Equalization (CLAHE) and a Python-based Graphical User Interface (GUI). The suggested method overcomes the difficulties of underwater image processing by leveraging the Haar Cascade Classifier for object detection, CLAHE for image enhancement, and the GUI for user-friendly interaction. The system is designed and built in Python, and the results are assessed using an underwater image dataset. The experimental findings demonstrate that the proposed method effectively detects and localizes the resources underwater. The user-friendly interface allows use of the system and executes underwater resource detection. The proposed system can be used to detect the underwater resources that are best suited for the conditions.