Current Volume 9
Forklift-related accidents are a major concern in industrial environments, especially in areas with shared pathways, blind spots, and high noise levels. These incidents often result in serious injuries or fatalities and disrupt operations. To address this issue, this project presents an “Industrial Forklift Alert and Anti-Collision System” designed to enhance workplace safety. The system uses photoelectric sensors to detect the presence of forklifts in real time. When a forklift is detected, a two-tier tower lamp provides visual alerts: a red light signals danger and instructs workers to stop, while a green light indicates safe conditions for movement. This simple, color-coded system ensures quick understanding and is especially effective in noisy environments where audio alerts may be missed. The system is fully automatic, cost-effective, and scalable, making it suitable for deployment across multiple zones in a facility. It improves safety by reducing the risk of collisions between forklifts and workers, promotes a culture of safety, and supports compliance with industrial safety standards such as OSHA. Initial trials have shown that the system significantly reduces accident risks, improves worker awareness, and minimizes downtime and injury-related costs. This project offers a practical and impactful solution for real-time hazard detection and safer forklift traffic management in industrial settings.
IRE Journals:
Avinash B. Yadav, Digvijay K. Pisal, Omkar V. Gitte, Shambhuraj Phadtare, Prof. A. A. Kekare "Industrial Forklift Alert & Anti-Collision System" Iconic Research And Engineering Journals Volume 9 Issue 12 2026 Page 36-40 https://doi.org/10.64388/IREV9I12-1718551
IEEE:
Avinash B. Yadav, Digvijay K. Pisal, Omkar V. Gitte, Shambhuraj Phadtare, Prof. A. A. Kekare
"Industrial Forklift Alert & Anti-Collision System" Iconic Research And Engineering Journals, 9(12) https://doi.org/10.64388/IREV9I12-1718551