Authors: Bhor Pranali Dinesh, Professor Bangar A. P., Professor Bhosale S.B, Professor Karad V. A
Abstract: Factory accidents continue to arise from lapses that are simple to describe but hard to police continuously: a worker without a helmet, a hand inside a press envelope, a person in the path of a moving vehicle. Manual supervision and periodic audits cannot watch every station at every moment. This paper presents the AI Factory Safety Monitor (AFSM), an edge-deployed computer-vision system that analyses ordinary industrial camera streams in real time to detect missing personal protective equipment (PPE), intrusions into configurable danger zones, and unsafe proximity to operating machinery. AFSM combines a single-stage object detector, a multi-object tracker, a pose-based posture check and a rule engine that converts raw detections into graded alerts using temporal filtering to suppress false alarms. Alerts are delivered locally (beacon, siren) and remotely (MQTT, SMS, supervisor dashboard), and a compliance-analytics layer records events for audit under occupational health and safety manage-ment practice. We describe the architecture, algorithms, deployment considerations and a proposed evaluation protocol, and discuss privacy, reliability and the limits of vision-based safety as a supplement to, not a replacement for, certified safety-rated controls.
