In recent days, people in remote area suffer a lot due to variety of natural calamities such as flooding, earthquake and so on. It has been noted that people used to stay in top portions of their house when there is a flooding issue. Hence, it is very difficult for the rescue team to identify the location of a person by looking at the parts of a person such as hands, legs and partial image of a face using the existing approaches. In this proposed approach, an idea for detecting person when there is only parts such as legs, hands are visible from remote, wild or non-urban areas with the help of UAV-Unmanned Aerial Vehicle has been suggested. Detecting person and identifying the location from the image tends to be a difficult process due to very small and camouflaged objects in the images collected. In this approach, Semantic Segmentation using deep learning approach has been applied in order to detect a person. SegNet- Segmentation Network is the network architecture used in the process of semantically segment the image according to each pixel, hence identifying person is easy. The main objective of this proposed model is that, sometime UAV image may contain partial person images, like legs, hand, etc., that could not be identified by existing approaches were being recognized and identified successfully. This model is trained and tested using HERIDAL dataset. Over 70% images were trained and 30% images were used for testing. This enhanced deep learning model named as Semantic SegNet model achieved an accuracy of 91.04%. This proposed Semantic SegNet model has been compared with existing approaches such as VGG16, GoogleNet and ResNet- Residual neural Network for the same set of trained and tested images. Comparison table declared that this proposed Semantic SegNet Model outperformed other existing models.

近年来，由于洪水，地震等各种自然灾害，边远地区的人们遭受了很多苦难。已经注意到，当有水灾问题时，人们通常呆在房屋的顶部。因此，对于救援队来说，通过使用现有方法观察诸如手，腿和面部局部图像之类的人的部分来识别人的位置非常困难。在这种提出的方​​法中，已经提出了一种在无人飞行器的帮助下从远处，野外或非城市区域仅能看到腿，手等部位时检测人的想法。由于收集的图像中的物体很小且被伪装，检测图像并从图像中识别位置往往是一个困难的过程。用这种方法 为了检测一个人，已经应用了使用深度学习方法的语义分割。SegNet分割网络是用于根据每个像素对图像进行语义分割的过程中使用的网络体系结构，因此识别人很容易。该模型的主要目的是，有时无人机图像可能包含部分人物图像，如腿，手等，这些图像无法被成功识别和识别的现有方法识别。使用HERIDAL数据集对该模型进行了训练和测试。训练了70％以上的图像，并使用30％的图像进行测试。这种名为语义SegNet模型的增强型深度学习模型达到了91.04％的准确性。将该提议的语义SegNet模型与现有方法（例如VGG16，GoogleNet和ResNet-残余神经网络，用于同一组经过训练和测试的图像。比较表表明，该提议的语义SegNet模型优于其他现有模型。