Safe Micro Aerial Vehicle (MAV) navigation requires detecting and avoiding obstacles. For safe MAV navigation, expansion‐based algorithms are effective for detecting obstacles. However, accurate and real‐time obstacle detection is a fundamental challenge. Some traditional methods focus on extracting geometric features from images and applying geometric constraints to identify potential obstacles. Others may leverage machine learning algorithms for object detection and classification, using features such as texture, shape, and context to distinguish obstacles from background clutter. The choice of approach depends on factors such as the specific requirements of the application, the complexity of the scene, and the available computational resources. Since obstacles, in reality, take the form of objects (e.g., persons, walls, pillars, trees, automobiles, and other structures), it is preferable to represent them according to human comprehension and as objects. Therefore, the objective of this study is to reflect on the previous research and address the issues mentioned above by extracting objects from the fisheye image using a panoptic deep‐learning network. The extracted object regions are, then, used to identify obstacles with a novel area‐based expansion rate we developed in a previous study. We compared the accuracy of obstacle detection in our proposed method to the existing method when moving forward and to the right; thus, we improved it between 10% and 18%, respectively. In addition, compared with the existing method, and due to replacing a single object with multiple regions, obstacle‐detection runtime for forward and right direction is 15.71 and 25.5 times faster, respectively, and the required match points have decreased by 49% and 55%.

中文翻译：

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使用全景分割开发基于扩展的障碍物检测

安全微型飞行器 (MAV) 导航需要检测并避开障碍物。对于安全的 MAV 导航，基于扩展的算法可以有效地检测障碍物。然而，准确、实时的障碍物检测是一个根本挑战。一些传统方法侧重于从图像中提取几何特征并应用几何约束来识别潜在障碍物。其他人可能会利用机器学习算法进行对象检测和分类，使用纹理、形状和上下文等特征来区分障碍物和背景杂乱。方法的选择取决于应用程序的具体要求、场景的复杂性以及可用的计算资源等因素。由于障碍物实际上采用物体的形式（例如，人、墙壁、柱子、树木、汽车和其他结构），因此最好根据人类的理解来将它们表示为物体。因此，本研究的目的是反思以前的研究，并通过使用全景深度学习网络从鱼眼图像中提取对象来解决上述问题。然后，提取的对象区域用于通过我们在之前的研究中开发的新颖的基于区域的扩展率来识别障碍物。我们将我们提出的方法与现有方法在向前和向右移动时的障碍物检测精度进行了比较；因此，我们分别将其提高了 10% 和 18%。此外，与现有方法相比，由于用多个区域替换单个物体，向前和向右方向的障碍物检测运行时间分别快了15.71和25.5倍，所需的匹配点减少了49%和55 %。