Not only the fish know when the fish tank is dirty; SingTown's water quality monitoring system sees the dirt and cleans automatically.

In water quality monitoring and ecological experiments, it is often necessary to determine whether the inner walls or bottom of containers are contaminated by substances such as sediment, silt, or algae, in order to assess water flow cleanliness and sediment changes.

Manual observation is subjective, time-consuming, and lacks continuous monitoring capability, making it difficult to meet the demands of automated water quality detection.

To this end, environmental monitoring equipment manufacturers adopt SingTown's OpenMV intelligent camera recognition technology, integrating AI machine vision and image recognition algorithms to achieve automatic identification and comparative analysis of contamination status at the bottom of transparent containers.

This system can identify various sediment attachments such as sand, silt, and green algae, and is suitable for application scenarios including water quality monitoring, ecological experiments, and river channel simulation systems.

Environmental monitoring equipment manufacturers utilise SingTown's OpenMV intelligent camera recognition technology to achieve automatic detection of contamination status in transparent containers and linkage with cleaning systems.

This solution installs a waterproof camera module on the exterior of a transparent acrylic container to perform real-time image acquisition of the bottom and sidewall areas. By employing colour distribution analysis, texture detection, and contrastive learning algorithms, it extracts features related to changes in brightness, saturation, and surface texture from the images to determine the presence of pollutant deposition.

When the container is in a clean state, the bottom area exhibits uniform colour distribution and high reflective brightness.

Once sediment, silt, or algae deposits appear, the image brightness decreases, texture roughness increases, and the colour distribution becomes darker.

The algorithm automatically calculates the pollution index and compares it with the initial "clean template" image. When the pollution level exceeds the threshold, the system outputs a "bottom dirtiness" signal.

OpenMV Embedded Vision Module, an edge AI image recognition chip, capable of completing sampling, analysis, and judgement within seconds without an external computer.

The system supports edge deployment and enables continuous monitoring. Identification results can be transmitted to the host computer via serial port or wireless communication, facilitating data logging and automated cleaning linkage.

Through this solution, environmental monitoring enterprises can achieve automatic identification, early warning, and maintenance alerts for the pollution status of transparent containers, significantly enhancing the automation and intelligence level of water quality detection and maintenance systems.