Underwater inspection and intervention dataset

Virtual Underwater Datasets for Autonomous Inspections

Underwater Vehicles have become more sophisticated, driven by the off-shore sector and the scientific community's rapid advancements in underwater operations. Notably, many underwater tasks, including the assessment of subsea infrastructure, are performed with the assistance of Autonomous Underwater Vehicles (AUVs). There have been recent breakthroughs in Artificial Intelligence (AI) and, notably, Deep Learning (DL) models and applications, which have widespread usage in a variety of fields, including aerial unmanned vehicles, autonomous car navigation, and other applications. However, they ar…

面向水下船舶检查的多标签视频分类

Multi-label Video Classification for Underwater Ship Inspection

本文针对水下船体检查中人工视频分析耗时费力的问题，提出一种基于Transformer自注意力机制的多标签视频分类模型，通过融合时空信息实现自动检测，为水下视频检查提供基准。

In-Hand Pose Estimation and Pin Inspection for Insertion of Through-Hole Components

The insertion of through-hole components is a difficult task. As the tolerances of the holes are very small, minor errors in the insertion will result in failures. These failures can damage components and will require manual intervention for recovery. Errors can occur both from imprecise object grasps and bent pins. Therefore, it is important that a system can accurately determine the object's position and reject components with bent pins. By utilizing the constraints inherent in the object grasp a method using template matching is able to obtain very precise pose estimates. Methods for pin-ch…

水下检测中图像分割的不确定性驱动主动学习

Uncertainty Driven Active Learning for Image Segmentation in Underwater Inspection

研究水下基础设施检测图像分割任务，提出基于互信息和Monte Carlo dropout的主动学习框架，显著降低标注成本并提升模型性能。

YOLOv8至YOLOv11在水下视觉任务中的性能比较研究

A Comparative Study of YOLOv8 to YOLOv11 Performance in Underwater Vision Tasks

本文对比了YOLOv8至YOLOv11在水下视觉任务中的性能，发现准确率在YOLOv9后饱和，而轻量级YOLOv10在嵌入式AUV部署中具有最佳的速度-准确率权衡。

Active Classification: Theory and Application to Underwater Inspection

We discuss the problem in which an autonomous vehicle must classify an object based on multiple views. We focus on the active classification setting, where the vehicle controls which views to select to best perform the classification. The problem is formulated as an extension to Bayesian active learning, and we show connections to recent theoretical guarantees in this area. We formally analyze the benefit of acting adaptively as new information becomes available. The analysis leads to a probabilistic algorithm for determining the best views to observe based on information theoretic costs. We v…