Edge-Computing for the Early Detection of Falls by People and/or Animals in Reservoirs

As a measure to sustain crops, the presence of irrigation, man-made reservoirs has become very common in regions affected by prolonged periods of low rainfall. Although these reservoirs must be provided with minimum safety facilities, it is also very common that animals or, to a much lesser extent, the people in charge of their maintenance, fall into the reservoir. The reservoirs then become, in most cases, a death trap, as, with plastic walls that are impossible to climb, they rarely have ramps to facilitate exit. This article describes the design of a proposed edge-computing module that, using embedded vision, identifies the fall of people and animals in irrigation reservoirs. The module includes 180-degree panoramic cameras with colour night vision capability and an NVIDIA Jetson Orin Nano Super. The lack of databases covering the problem to be solved has been addressed by generating synthetic videos showing animals or people falling into irrigation reservoirs. The effectiveness of the training carried out using these synthetic sequences has subsequently been successfully validated using images captured in real-world environments.