Real-time object detection in resource-constrained environments poses significant challenges due to the limited computational power of controller units (CUs). Traditional single-controller approaches struggle to process multiple object attributes simultaneously, leading to increased inference latency and reduced efficiency. To address this, we propose a multi-controller TinyML-based architecture where two CUs operate collaboratively each identifying different parameters. Upon detection, one CU transmits its result to the second CU through UART protocol, consolidating both parameters and displaying the final classification output. The final results are then transmitted via TCP or UDP, depending on network traffic conditions, to ensure efficient data exchange. This paper also explores various real-world applications where multiple CUs collaboratively enhance object detection efficiency in resource-limited environments.