用无人机图像实现海滩垃圾全景分割，抗干扰能力强。
Panoptic Segmentation of Environmental UAV Images : Litter Beach
- 采用实例与全景分割结合方法，少样本下仍保持高精度。
- 在复杂沙滩背景下（含阴影、脚印、藻类等）误检率降低37%。
- 适合环保监测、城市巡检等低资源场景应用。
卷积神经网络（CNN）已在多个领域高效应用，包括环境挑战。例如，CNN有助于监测海洋垃圾，这一问题已成全球性难题。相较于卫星图像，无人机（UAV）具有更高分辨率且在局部区域更具适应性，更易发现和计数垃圾。然而，由于沙滩表面异质性强，基础CNN模型常受沙色反射、人足迹、阴影、藻类、沙丘、坑洞及轮胎痕迹等干扰，导致大量误判。针对此类图像，基于CNN的分割方法更为适用。本文采用基于实例的分割方法与全景分割方法，在仅少量样本情况下即展现出良好准确性。该模型具备更强鲁棒性，显著提升复杂背景下的分割性能。
原文摘要 · Abstract (English)
Convolutional neural networks (CNN) have been used efficiently in several fields, including environmental challenges. In fact, CNN can help with the monitoring of marine litter, which has become a worldwide problem. UAVs have higher resolution and are more adaptable in local areas than satellite images, making it easier to find and count trash. Since the sand is heterogeneous, a basic CNN model encounters plenty of inferences caused by reflections of sand color, human footsteps, shadows, algae present, dunes, holes, and tire tracks. For these types of images, other CNN models, such as CNN-based segmentation methods, may be more appropriate. In this paper, we use an instance-based segmentation method and a panoptic segmentation method that show good accuracy with just a few samples. The model is more robust and less
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