The compliant construction of school swimming venues is often more complex than imagined. In addition to conventional water quality, lifeguard staffing, and emergency plans, local authorities have introduced new requirements for intelligent safety supervision in recent years. Many schools we have worked with, while promoting the popularization of swimming lessons, are most worried not about teaching itself but about those few minutes of 'blind spots'—a child who chokes on water can sink in just ten to twenty seconds. This is why AI drowning prevention projects are being incorporated into the plans of more and more schools.
A complete implementation process roughly begins with compliance evaluation. First, you need to check the latest documents from local education and sports departments to confirm whether the venue's hardware conditions meet the standards, such as water depth markers, drain outlet protection, and surveillance camera coverage angles. This step is not just a formality, because the installation location and recognition accuracy of the AI system are closely related to the physical layout of the venue. If the pool floor has uneven lighting or there are fixed obstructions, even the best algorithm will be affected.
Next comes solution design. We usually suggest that schools first clarify their own needs: whether to only have real-time monitoring of the pool area, or to also include public areas such as changing rooms and showers. AI drowning prevention mainly relies on underwater cameras and edge computing devices, and can identify actions such as prolonged stillness, abnormal sinking, waving, and struggling. However, because the dimensions and water quality of each venue are different, the number of cameras, installation depth, and focal length all need to be customized. This step requires repeated communication with the construction team and software supplier, and it is best to arrange an on-site survey and simulation test.
During the construction and commissioning phase, the most critical thing is not to affect normal teaching. Many schools choose to carry out construction during holidays or gaps in the swimming schedule, using nighttime to fix underwater equipment and route cables. During debugging, special attention should be paid to the impact of light changes on recognition accuracy. For example, strong reflections on sunny afternoons or different color temperatures of night lighting may cause the algorithm to produce false alarms. Our experience is to set up a 'manual review' mode for the first two weeks, where alarms are first confirmed by lifeguards or duty teachers before gradually increasing the proportion of automatic system response.
Finally, there is usage training and emergency linkage. No matter how intelligent the AI system is, it cannot replace lifeguards. Its value lies in adding an extra pair of 'eyes that never blink' to help staff spot danger at the first moment. Therefore, when accepting the project, it is essential to organize practical drills for all physical education teachers and lifeguards, so that the entire chain of alarm triggering, information push, on-site rescue, and follow-up records runs smoothly. Only when these steps are all working well can AI drowning prevention truly be integrated into the campus safety system.