论文标题
自动检测基于空间一致性的深网基于深网的伊蚊繁殖地
Automatic Detection of Aedes aegypti Breeding Grounds Based on Deep Networks with Spatio-Temporal Consistency
论文作者
论文摘要
每年，埃及埃及蚊子都会感染数百万人，例如登革热，Zika，Chikungunya和Urban黄热病。对抗这些疾病的主要形式是通过寻找和消除潜在的蚊子繁殖地来避免蚊子繁殖。在这项工作中，我们介绍了一个综合的航空视频数据集，该数据集用无人驾驶飞机购买，其中包含可能的蚊子繁殖地。视频数据集的所有帧均已手动注释，并用识别所有感兴趣的对象的边界框进行注释。该数据集被用来开发基于深卷积网络的此类对象的自动检测系统。我们建议对视频中包含的时间信息的开发，该集合在对象检测管道中的时空一致性模块中，该模块可以注册被检测到的对象，从而最大程度地降低了最大阳性和假阴性的出现。另外，我们通过实验表明，使用视频比仅使用框架组成马赛克更有益。使用Resnet-50-FPN作为骨干，我们分别在对象级检测“轮胎”和“水箱”上实现了0.65 $ _1 $ -SCOORS和0.77，这说明了系统能力，以正确地找到潜在的蚊子育种对象。
Every year, the Aedes aegypti mosquito infects millions of people with diseases such as dengue, zika, chikungunya, and urban yellow fever. The main form to combat these diseases is to avoid mosquito reproduction by searching for and eliminating the potential mosquito breeding grounds. In this work, we introduce a comprehensive dataset of aerial videos, acquired with an unmanned aerial vehicle, containing possible mosquito breeding sites. All frames of the video dataset were manually annotated with bounding boxes identifying all objects of interest. This dataset was employed to develop an automatic detection system of such objects based on deep convolutional networks. We propose the exploitation of the temporal information contained in the videos by the incorporation, in the object detection pipeline, of a spatio-temporal consistency module that can register the detected objects, minimizing most false-positive and false-negative occurrences. Also, we experimentally show that using videos is more beneficial than only composing a mosaic using the frames. Using the ResNet-50-FPN as a backbone, we achieve F$_1$-scores of 0.65 and 0.77 on the object-level detection of `tires' and `water tanks', respectively, illustrating the system capabilities to properly locate potential mosquito breeding objects.
