[论文解读] Intelligent Debris Mass Estimation Model for Autonomous Underwater Vehicle

Mohana Sri S, S Swethaa+2 more|arXiv (Cornell University)|Sep 19, 2023

Water Quality Monitoring TechnologiesEnvironmental Science被引用 3

一句话总结

本文提出了一种基于YOLOv7实例分割的智能沉船残骸质量估算模型，用于自主水下航行器（AUVs）从水下图像中检测和分割海洋垃圾。通过应用二值掩码、形态学后处理以及蒙特卡洛面积估算方法，该方法实现了对总垃圾面积和质量的精确估算，提升了AUV在环境清理任务中的能力。

ABSTRACT

Marine debris poses a significant threat to the survival of marine wildlife, often leading to entanglement and starvation, ultimately resulting in death. Therefore, removing debris from the ocean is crucial to restore the natural balance and allow marine life to thrive. Instance segmentation is an advanced form of object detection that identifies objects and precisely locates and separates them, making it an essential tool for autonomous underwater vehicles (AUVs) to navigate and interact with their underwater environment effectively. AUVs use image segmentation to analyze images captured by their cameras to navigate underwater environments. In this paper, we use instance segmentation to calculate the area of individual objects within an image, we use YOLOV7 in Roboflow to generate a set of bounding boxes for each object in the image with a class label and a confidence score for every detection. A segmentation mask is then created for each object by applying a binary mask to the object's bounding box. The masks are generated by applying a binary threshold to the output of a convolutional neural network trained to segment objects from the background. Finally, refining the segmentation mask for each object is done by applying post-processing techniques such as morphological operations and contour detection, to improve the accuracy and quality of the mask. The process of estimating the area of instance segmentation involves calculating the area of each segmented instance separately and then summing up the areas of all instances to obtain the total area. The calculation is carried out using standard formulas based on the shape of the object, such as rectangles and circles. In cases where the object is complex, the Monte Carlo method is used to estimate the area. This method provides a higher degree of accuracy than traditional methods, especially when using a large number of samples.

研究动机与目标

• 开发一种智能模型，使自主水下航行器（AUVs）能够在海洋环境中估算垃圾质量。
• 解决在低能见度和杂乱的水下场景中实现垃圾准确检测与分割的挑战。
• 通过精确估算垃圾体积和质量，提升AUV的导航与干预能力。
• 利用深度学习与图像处理技术，实现实时水下作业中的垃圾评估。
• 通过提供一种可扩展、自动化的解决方案，支持海洋保护工作，实现海洋清理任务中垃圾质量的估算。

提出的方法

• 在Roboflow中使用YOLOv7生成水下图像中海洋垃圾的物体检测边界框，附带类别标签和置信度分数。
• 应用卷积神经网络，通过对模型输出进行二值阈值处理，生成分割掩码。
• 利用形态学操作和轮廓检测对分割掩码进行优化，以提高准确度并减少噪声。
• 使用简单形状（如矩形、圆形）的几何公式分别计算每个分割垃圾实例的面积。
• 对复杂形状的垃圾采用蒙特卡洛方法进行面积估算，采样次数越高，精度越高。
• 将各个实例的面积相加，计算总垃圾面积，并据此基于假设的密度估算质量。

实验结果

研究问题

• RQ1 YOLOv7结合实例分割在真实水下图像中检测和分割海洋垃圾的准确性如何？
• RQ2 形态学操作和轮廓检测等后处理技术在多大程度上提升了分割掩码的质量？
• RQ3 蒙特卡洛方法在估算不规则形状垃圾面积方面与几何公式相比表现如何？
• RQ4 集成模型能否可靠地从图像数据中估算出AUV海洋清理任务的总垃圾质量？
• RQ5 图像质量和环境条件对垃圾质量估算流水线性能有何影响？

主要发现

• YOLOv7与实例分割的结合实现了水下图像中海洋垃圾的高精度检测与定位。
• 形态学操作等后处理技术显著提升了掩码质量，有效减少了噪声并填补了分割区域的空隙。
• 与几何近似相比，蒙特卡洛方法在复杂形状垃圾的面积估算中表现更优，尤其在高采样率下精度更高。
• 通过累加各个实例的面积，计算出总垃圾面积，为质量估算提供了可靠基础。
• 所提出的模型在AUV应用中展示了实时垃圾质量估算的可行性，支持自主环境监测与清理。
• 该方法在处理多样的垃圾形状和水下视觉条件方面表现出鲁棒性，增强了AUV的自主运行能力。