PAW: Prediction of wildlife animals using a robot under adverse weather conditions

Abstract

Image dehazing and object detection are two different research areas that play a vital role in machine learning. When merged together and implemented in real-time, it is a boon in the field of artificial intelligence, specifically robotics. Object detection and tracking are two of the major implementations in almost the entire robot's training and learning. The learning of the robot depends on the images; these images can be camera-captured images or a pretrained data set. Real-time outdoor images clicked in bad weather conditions, such as mist, haze, smog, and fog, often suffer from poor visibility, and the consequences are incorrect results and hence an unexpected robot's behavior. To overcome these consequences, we have presented a novel approach to object detection and identification during adverse weather conditions. This method is proposed to be implemented in a real-time environment to monitor animal behavior near railway tracks during fog, haze, and smog. This is not limited to specific application areas but can be used to identify endangered species and take active steps to save them from mishap. The deployment is done in a real-time indoor environment using Tortoisebot mobile robot with a robot operating system framework.