Development of Vision-Based Load and Configuration Safety Monitoring System for Mast-Climbing Work Platforms

The Centers for Disease Control and Prevention intends to award a sole-source firm fixed-price contract to Stony Brook University (SUNY) for the development of a lightweight machine learning system to monitor the safety of mast-climbing work platforms (MCWPs). The work centers on a vision-based approach for monitoring platform load and configuration to support construction safety applications.

According to the notice, SUNY is identified as the only source capable of performing the requirement because it is the sole custodian of the proprietary dataset underlying the preliminary analysis and the institution that originally generated the dataset. The agency states that no other vendors have been identified with the necessary dataset access, institutional knowledge of the original study design, and specialized capability in dataset collection and machine learning modeling.

This notice is issued under the authority cited in FAR Part 6.302-1 as a sole-source acquisition and is not a request for competitive proposals. Firms that believe they can meet the requirement may submit a written capability statement referencing solicitation number 75D30126Q79175 within 15 business days of the original publication date for agency consideration.