Safety Helmet Detection Based On Deep Learning
Disha Sanjay Patil, Swarupa Pankaj Patil, Darshana Kaivalya Patil, Bhagyashri Dilip Patil, Prof. Vinayak Patil
Deep Learning, CNN, YOLOV3.
Safety helmet detection is a crucial task in ensuring workplace safety, especially in hazardous environments such as construction sites and industrial facilities. Traditional methods for safety helmet detection often rely on handcrafted features and are limited in their robustness and accuracy. In recent years, deep learning techniques, particularly convolutional neural networks (CNNs), have shown promising results in various computer vision tasks, including object detection. In this paper, we propose a deep learning-based approach for safety helmet detection using CNNs. We present a dataset of annotated images containing individuals wearing safety helmets, which is used to train and evaluate our model. Our approach involves fine-tuning a pre-trained CNN architecture on the safety helmet dataset to leverage its ability to extract high-level features from images. We experiment with different CNN architectures and training strategies to optimize the detection performance. Furthermore, we conduct extensive evaluation experiments on both synthetic and real-world data to demonstrate the effectiveness and robustness of our approach. The experimental results show that our proposed method achieves high accuracy and robustness in safety helmet detection, outperforming traditional methods and demonstrating its potential for practical applications in improving workplace safety.
Article Details
Unique Paper ID: 164851

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 2505 - 2509
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