Abstract:
Marine debris, a hazardous threat to the marine environment, has emerged as a critical global environmental issue. The commonly applied method to remove marine debris fro...Show MoreMetadata
Abstract:
Marine debris, a hazardous threat to the marine environment, has emerged as a critical global environmental issue. The commonly applied method to remove marine debris from the marine environment is manual removal. One solution to this problem is to use deep learning-based visual marine debris detectors to automatically detect marine debris. However, in terms of marine debris detection, there are several challenges, one of which is because the complex marine environment makes visual detectors able to detect marine debris accurately and in real-time so as not to damage the marine environment. In this research, three variants of the RTMDet (Real-Time Models for Object Detection) model were trained and evaluated using the TrashCan-Instance dataset. One of them is the RTMDet-1 model with improvisation to replace the loss bbox in the head model with DIoU (Distance-Intersection over Union) and improvisation to add a sampling strategy with a Class-aware Sampling technique to handle the imbalanced data problem that has obtained mAP50(Mean Average Precision at threshold 50%) accuracy of 71.3%. This has made the model's best object detection accuracy on the TrashCan-Instance dataset while maintaining detection speed. These results prove that the model proposed in this study can be a vital consideration for further development in detecting marine debris. This contribution aims to address the global challenges related to marine debris and stimulate the development of more effective and efficient object detection models in complex marine environments.
Date of Conference: 10-11 July 2024
Date Added to IEEE Xplore: 05 September 2024
ISBN Information: