Towards real-time video analysis of flooded areas: redundancy-based accelerator for object detection models

The state of Kerala in India has seen multiple instances of intense cyclones in recent years, resulting in heavy flooding. One of the biggest challenges faced by rescuers is the accessibility to flooded areas and buildings during rescue operations. In such scenarios, unmanned aerial vehicles (UAVs) can deliver reliable aerial visual data to aid planning and operations during rescue. Object detectors based on deep learning methods provide an effective solution to automate the process of detecting relevant information from image/video data. These models are complex and resource-hungry, leading to severe speed constraints during field operations. The pixel displacement algorithm (PDA), a portable and effective technique, is developed in this work to speed up object detection models on devices with limited resources, such as edge devices. This method can be integrated with all object detection models to speed up the inference time. The proposed method is combined with multiple object detection models in this work to show its effectiveness. The YOLOv4 model combined with the proposed method outperformed the AP50 performance of the YOLOv4-tiny model by 6 \(\%\) $\%$ while maintaining the same processing time. This approach gave almost 10 \(\times \) $\times$ speed improvement to Jetson Nano at an accuracy cost of \(3\%\) $3\%$ when compared to YOLOv4. Further, a model to predict maximum pixel shift with respect to frame skip is proposed using parameters such as the altitude and velocity of the UAV and the tilt of the camera. Accurate prediction of pixel shift leads to a reduced search area, leading to reduced inference time. The effectiveness of the proposed model was tested against annotated locations, and it was found that the method was able to predict the search area for each test video segment with a high degree of accuracy.