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、饥饿甚至死亡。因此，清除海洋中的碎片对于恢复自然生态平衡、保障海洋生物繁衍至关重要。实例分割作为目标检测的高级形式，不仅能识别物体，还能精准定位并分离目标，使自主水下航行器（AUV）能够有效导航并与水下环境交互。AUV利用图像分割分析摄像头捕获的图像，从而在水下环境中导航。本文采用实例分割计算图像中单个物体的面积：使用Roboflow平台上的YOLOV7为图像中每个物体生成一系列边界框，并附上类别标签和置信度分数；随后通过将二元掩膜应用于物体边界框，为每个物体生成分割掩膜。该掩膜由经过训练的卷积神经网络输出经二元阈值处理得到，用于从背景中分割物体。最后，通过形态学运算和轮廓检测等后处理技术优化每个物体的分割掩膜，以提高掩膜的准确性与质量。实例分割的面积估算过程包括：分别计算每个分割实例的面积，再累加所有实例面积以获取总面积。计算基于物体形状（如矩形和圆形）采用标准公式进行。对于复杂形状物体，则采用蒙特卡洛方法进行面积估算。该方法相比传统方法具有更高精度，尤其在样本量较大时效果显著。