论文标题
注意神经网络在水道上检测垃圾
Attention Neural Network for Trash Detection on Water Channels
论文作者
论文摘要
流过城市的河流和运河通常被非法用于倾倒垃圾。这会污染淡水通道,并在下水道中造成阻塞,从而导致城市洪水。当这种被污染的水到达农田时,它会导致土壤退化,并构成关键的环境和经济威胁。倾倒垃圾经常被发现漂浮在水面上。垃圾可能会被毁容,部分淹没,分解成较小的碎片,与其他遮盖其形状的物体结合在一起,并产生了具有挑战性的检测问题。本文提出了一种检测到城市地区水表面上漂浮的可见垃圾的方法。我们还提供了一个大型数据集,首先是水道中包含对象级注释的垃圾。提出了一个新颖的注意力层,以改善对较小物体的检测。在本文结束时,我们将方法与最先进的对象检测器进行了详细的比较,并表明我们的方法显着改善了对较小对象的检测。该数据集将公开可用。
Rivers and canals flowing through cities are often used illegally for dumping the trash. This contaminates freshwater channels as well as causes blockage in sewerage resulting in urban flooding. When this contaminated water reaches agricultural fields, it results in degradation of soil and poses critical environmental as well as economic threats. The dumped trash is often found floating on the water surface. The trash could be disfigured, partially submerged, decomposed into smaller pieces, clumped together with other objects which obscure its shape and creates a challenging detection problem. This paper proposes a method for the detection of visible trash floating on the water surface of the canals in urban areas. We also provide a large dataset, first of its kind, trash in water channels that contains object-level annotations. A novel attention layer is proposed that improves the detection of smaller objects. Towards the end of this paper, we provide a detailed comparison of our method with state-of-the-art object detectors and show that our method significantly improves the detection of smaller objects. The dataset will be made publicly available.