School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; Key Laboratory of Tourism Multisource Data Perception and Decision, Ministry of Culture and Tourism (TMDPD, MCT), Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Mar Pollut Bull. 2024 Nov;208:117019. doi: 10.1016/j.marpolbul.2024.117019. Epub 2024 Sep 25.
The unmanned aerial vehicle (UAV) is usually flexible and frequently low-altitude flying without the influence of clouds and severe weather, and it is widely used for port oil spill detection (OSD). However, the background of the port is usually complex, the oil spills in UAV images are usually small and irregular, as well as the oil boundary is fuzzy, which has led to the failure of existing methods in accurately detecting the port oil spill. Here, we propose a scene-class guided dual branch network for port OSD based on UAV images, which can locate the oil spill areas of different sizes and suppress the influence caused by complex backgrounds. Specifically, the dual-branch network consists of semantic segmentation and image classification branches. The image classification branch utilizes the scene-class as the label and further can extract the feature attention, which can guide the semantic segmentation branch to learn the key area features. Second, we propose a multi-scale arbitrary shape convolution module, which can address the challenges caused by fuzzy oil boundaries and irregular small objects. Finally, due to the imbalance between oil spill pixels and other pixels, we design a joint loss to optimize the network. We evaluate our proposed method on a public UAV OSD dataset. The results show that our method is superior to the state-of-the-art method, achieving mIoU of 90.22 %, A of 96.03 %, P of 91.99 %, R of 92.56 %, and F1 of 92.28 %, which represents the feasibility of our method in port OSD and its potential to save a lot of manpower and material resources. The ablation experiment further demonstrates the effectiveness of each designed part.
无人驾驶飞行器（UAV）通常具有灵活性，并且频繁地在低海拔飞行，不受云层和恶劣天气的影响，因此被广泛应用于港口溢油检测（OSD）。然而，港口的背景通常较为复杂，UAV 图像中的溢油通常较小且不规则，并且油边界较为模糊，这导致现有的方法无法准确地检测到港口溢油。在这里，我们提出了一种基于 UAV 图像的港口 OSD 场景引导双分支网络，该网络可以定位不同大小的溢油区域，并抑制复杂背景的影响。具体来说，双分支网络由语义分割和图像分类分支组成。图像分类分支利用场景类别作为标签，并进一步提取特征注意力，这可以指导语义分割分支学习关键区域特征。其次，我们提出了一种多尺度任意形状卷积模块，可以解决模糊油边界和不规则小物体带来的挑战。最后，由于溢油像素和其他像素之间存在不平衡，我们设计了联合损失来优化网络。我们在一个公共的 UAV OSD 数据集上评估了我们提出的方法。结果表明，我们的方法优于最先进的方法，在 mIoU 上达到了 90.22%，A 上达到了 96.03%，P 上达到了 91.99%，R 上达到了 92.56%，F1 上达到了 92.28%，这表明了我们的方法在港口 OSD 中的可行性及其在节省大量人力和物力资源方面的潜力。消融实验进一步证明了每个设计部分的有效性。
