Two-stage safety helmet detection in industrial environment using YOLO models
Publish place: 1th conference on the opportunities and challenges of artificial intelligence and new technologies in industry and mining
Publish Year: 1403
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
AITIM01_048
تاریخ نمایه سازی: 14 مرداد 1403
Abstract:
Ensuring that workers in the construction and manufacturing sectors wear helmets is crucial for preventing workplace accidents. This paper presents a two-stage instance detection method for determining helmet compliance using YOLO models. In the first stage, a YOLO with COCO dataset is utilized to detect humans within images. In the second stage, the detected human head positions are classified into "helmet" and "no helmet" categories using a specialized classification model. Our study compares the performance of YOLOv۵, YOLOv۸, and YOLOv۹ models, with results indicating that YOLOv۸ achieves the highest Precision and Recall and F۱. To further enhance accuracy, a confidence threshold is implemented in the second stage; frames where the model's confidence is insufficient are skipped. This method significantly improves the detection of helmet usage, providing a reliable tool for enhancing safety compliance and reducing the risk of injuries in industrial environments.
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Authors
Poorya Khorsandy
Khorramshahr University of Marine Science and Technology
Seyed Saeed Hayati
Khorramshahr University of Marine Science and Technology