Deep Learning-Based Workers Safety Helmet Wearing Detection on Construction Sites Using Multi-Scale Features

Aim:

      The aim of this study is to detect weapon by employing YOLO v8, focusing on better accuracy.

Abstract:

        Due to a lack of knowledge about safety helmets, accidents and injuries on construction sites are now increasingly common. Worker supervision by hand is challenging and ineffective. Workers often take off the helmets because of weak security-conscious and discomfort, then hidden dangers will be brought by this behaviour. Workers without safety helmets will suffer more injuries in accidents such as falling human body and vertical falling matter. Hence, detecting safety helmet wearing is a vital step of construction sites safety management and a safety helmet detector. However, traditional manual monitor is labour intensive and methods of installing sensors on safety helmet are difficult to popularize.This study is visually checking the construction site to see if anyone is wearing a safety helmet. In order to recognise a safety helmet in real time at a building site, we built a deep learning-based technique.

Existing System:

     Smaller Objects Detection: YOLOv3 can struggle to detect smaller objects due to its anchor box design and large stride. High Memory Requirements: YOLOv3 requires a significant amount of memory to run, which can be a challenge for devices with limited resources

      Training Time: Training YOLOv3 can be time-consuming, requiring large amounts of data and computational resources.

Problem Definition:

      The problem is to detect the helmet accurately. The accuracy of detecting helmet is low so, we need to improve the accuracy to enhance performance.

Proposed Method:

      YOLOv8 is the latest version of the YOLO object detection model, and it has several advantages over previous versions. YOLOv8 is more accurate and efficient than other algorithms, and it’s easier to train than its predecessors. YOLOv8 also requires fewer parameters to achieve its performance, making it a more streamlined and efficient model.

Advantages:                      

• Enhanced Accuracy: YOLO v8 demonstrates improved accuracy in detecting.
• Increased Speed: YOLO v8 is known for its faster processing