Developing a low-altitude inspection visual recognition system and determining the exact budget required is indeed a question that is hard to answer in one sentence. Cost differences can be huge depending on the scenarios and the depth of functionalities. However, we can break down the main expenses to get a rough idea.
First is the hardware. If the flying platform is an off-the-shelf drone, the main considerations include the onboard computing unit, high-resolution visible-light camera, and optional thermal imaging or LiDAR. Depending on the selection, this portion can range from tens of thousands to hundreds of thousands of yuan. Customized mounting or dustproof and waterproof modifications will incur additional costs. On the ground side, a high-performance workstation is also needed for model training and post-data processing, which is another considerable investment.
Software and algorithms are the core of the budget. If you are just calling open-source models for simple recognition, the cost mainly lies in integration and tuning. However, for specific inspection targets—such as missing pins on transmission lines, tiny cracks on wind turbine blade surfaces, or leaks in chemical plant pipelines—you need to collect a large amount of real-scene data, annotate it, and train the models. Data collection requires multiple field flights, and annotation is time-consuming and labor-intensive, which often becomes the largest hidden cost. Algorithm development also includes continuous optimization of recognition accuracy, balancing false positives and false negatives, and accelerating deployment on edge devices.
In addition to development, post-launch maintenance should also be taken into account. Inspection scenarios are complex, and changes in lighting, weather, and angles can affect recognition performance, so the model needs to be iterated with new data regularly. The daily operation and maintenance of the software platform, as well as inspection and calibration of drone hardware, are also ongoing costs.
Overall, a low-altitude inspection visual recognition system that can be practically deployed and achieve good recognition results typically starts with a development budget of several hundred thousand yuan. Complex industry-customized solutions can reach millions or even higher. The key is not to blindly compress the budget, but to clarify the core inspection requirements and distinguish which functions are essential and which can be expanded later. Starting with a small-scale pilot to validate the technical approach and actual performance, then gradually scaling up, is a more prudent approach. Reasonable budget allocation is far more important than simply pursuing a low number.