Enhancing underwater object detection through water artifact removal and using ensemble transfer learning

Abstract

The utilization and exploration of deep-sea resources has made underwater autonomous operation increasingly important to mitigate the dangers of the highpressure deep-sea environment. Intelligent computer vision plays a crucial role in underwater autonomous operation, and pre-processing procedures such as weak illumination and low-quality image enhancement are necessary for underwater vision. Underwater object detection plays a critical role in various domains such as marine biology, environmental monitoring, and underwater robotics. However, it is a challenging task due to the complexities of the underwater environment, including poor visibility, light attenuation, and color distortion. In this research paper, we propose a comprehensive methodology for underwater object detection using transfer learning with PyTorch and Jetson Inference. The contributions of this research paper include advancements in underwater object detection through the combination of transfer learning, fine-tuning, and optimization techniques. The utilization of PyTorch and Jetson Inference frameworks provides a powerful and efficient platform for implementing and deploying the model. Additionally, the incorporation of image-clearing techniques ensures the quality of the dataset and improves the model’s performance in challenging underwater conditions. The results of this research have practical implications for a variety of underwater applications, including marine environment monitoring, underwater exploration, and underwater autonomous robots for visual data collection in complex scenarios. By accurately detecting and classifying underwater objects, our methodology contributes to the understanding and preservation of underwater ecosystems, enhancing the capabilities of underwater systems and facilitating decision-making processes. Future work in this field may involve exploring different architectures, incorporating additional data augmentation techniques, and further fine-tuning the model with larger and more diverse underwater datasets. These efforts will contribute to advancing the state-of-the-art in underwater object detection, enabling more robust and efficient solutions for a wide range of underwater applications. .