Kisaezehra¹, Muhammad Umer Farooq^1,*, Muhammad Aslam Bhutto², Abdul Karim Kazi¹

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 911-927, 2023, DOI:10.32604/iasc.2023.031359

Abstract The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety (OHS) is of prime importance. Like in other developing countries, this industry pays very little, rather negligible attention to OHS practices in Pakistan, resulting in the occurrence of a wide variety of accidents, mishaps, and near-misses every year. One of the major causes of such mishaps is the non-wearing of safety helmets (hard hats) at construction sites where falling objects from a height are unavoidable. In most cases, this leads to serious brain injuries in people present at… More >

Qingyang Zhou¹, Jiaohua Qin^1,*, Xuyu Xiang¹, Yun Tan¹, Neal N. Xiong²

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 159-174, 2021, DOI:10.32604/cmc.2021.017480

Abstract The existing safety helmet detection methods are mainly based on one-stage object detection algorithms with high detection speed to reach the real-time detection requirements, but they can’t accurately detect small objects and objects with obstructions. Therefore, we propose a helmet detection algorithm based on the attention mechanism (AT-YOLO). First of all, a channel attention module is added to the YOLOv3 backbone network, which can adaptively calibrate the channel features of the direction to improve the feature utilization, and a spatial attention module is added to the neck of the YOLOv3 network to capture the correlation between any positions in the… More >