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.

翻译：海洋碎片对海洋野生动物的生存构成重大威胁，常导致其缠绕、饥饿乃至死亡。因此，清除海洋中的碎片对于恢复自然平衡、促进海洋生物繁衍生息至关重要。实例分割是目标检测的进阶形式，能够识别目标并精确定位、分离它们，这使其成为自主水下航行器（AUV）有效导航和与水下环境交互的重要工具。AUV通过图像分割分析摄像头捕获的图像，以导航水下环境。本文利用实例分割计算图像中单个物体的面积：首先通过Roboflow平台中的YOLOV7为图像中每个物体生成一组带有类别标签和置信度得分的边界框；然后对每个边界框应用二值掩膜，生成该物体的分割掩膜。这些掩膜通过对卷积神经网络（该网络用于从背景中分割物体）的输出施加二值阈值来生成；最后，通过形态学运算和轮廓检测等后处理技术对每个物体的分割掩膜进行细化，以提高掩膜的精度和质量。实例分割面积估算过程包括分别计算每个分割实例的面积，然后对所有实例的面积求和得到总面积。计算基于物体形状（如矩形和圆形）的标准公式进行。对于形状复杂的物体，则采用蒙特卡洛方法估算面积。与传统方法相比，该方法（尤其是在使用大量样本时）能提供更高的精度。