Investigation of the prediction of wildlife animals and its deployment using the robot

Abstract

AbstractMonitoring Wildlife in their natural habitat requires direct human intervention. Some animals are scared of humans. In such situations, camera‐equipped devices are implemented to gain a clear picture of Wildlife. Objective: Current wildlife detection models detect and classify the animal from camera‐captured images, limiting the action to rescue or save them from mishaps. Also, the camera‐equipped devices are fixed at particular locations. Therefore, an efficient detection model capable of protecting the animal has potential to play an important role. Method: To this end, we present Pred‐WAR, a Convolution Neural Network (CNN)‐based image classification approach to detect and raise rescue alerts for real‐time Wildlife. In our approach, we have proposed a Mask Region‐based CNN (Mask RCNN or MRCNN) with an Automatic Mixed Precision model that is implemented on a Robot Operating System‐based mobile robot with Raspberry Pi4 to detect and raise acoustic of Lion or alarm to alert or rescue animal in real‐time. Results: Pred‐WAR obtained a mean Average Precision value of 85.47% and an F1 score of 87.73% with a precision value range between 92% to 99%, outperforming the current MRCNN model. Significance: This approach has fast computation speed and maintains accuracy that will be efficiently implemented in real‐time scenarios.