Enhancing helmet and cigarette detection in electricity power construction based on Yolov5s-I algorithm

Abstract

This paper presents a Yolov5s-I algorithm to enhance the helmet and cigarette detection model in power construction. This study focuses on improving the performance of the popular YOLOv5s object detector in detecting safety helmets and cigarette, specifically in Electricity power construction. We investigated how we can modify the YOLOv5s model to make it better at detecting these small objects. We observed that by replacing certain parts of the model and adjusting their connections and settings, we can enhance its ability to detect safety helmets and cigarette. This model show an improvement in detecting smaller objects with a 50% Intersection over Union (IOU) threshold. Our main objective is to provide insights for future research on how to adapt popular detectors like YOLOv5 for specific tasks, such as detecting safety helmets and cigarette smoking in power construction sites. By making these changes, autonomous systems used in such settings can have access to more detailed information, thereby improving their effectiveness and safety.