• Anisha Prabhu
• Dr. Jaychand Upadhyay
• Tanvi Bhabal
• Farhan Khan
• Sudeep Poojary

Helmet use among the motorcyclists is of paramount importance in ensuring safety on the road and is a crucial factor in reducing head injuries. There is a need for a new approach in this regard. This study, therefore, focuses on an innovative method that integrates advanced technology with a helmet detection system to investigate the capability of deploying these advanced algorithms, such as YOLO, FOMO, ResNet50, and MobileNet50, with an ESP32 camera platform. It aims to develop an automatic system for real-time detection of people wearing helmets to ensure compliance with this safety measure. Utilizing the ESP32 camera to capture image data and perform tasks, such as object detection and classification, will significantly strengthen road safety measures and reduce motorcycle-related accidents. In this study, the design, implementation, and evaluation of the system will be explained so that its potential in enhancing traffic safety and mitigating death resulting from road accidents will become apparent. Utilizing both ESP32 and Raspberry Pi makes the system even more effective and stronger, greatly contributing to road safety.

Helmet Detection, ESP32, Mobilenet, Resnet50, YoloV8, FOMO, Raspberry Pi

"Helmet Detection using ESP 32 Camera and Raspberry Pi", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.q64-q71, April-2024, Available :http://www.jetir.org/papers/JETIR2404H08.pdf

"Helmet Detection using ESP 32 Camera and Raspberry Pi", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppq64-q71, April-2024, Available at : http://www.jetir.org/papers/JETIR2404H08.pdf