With the increasing awareness of safety production,construction site safety supervision is increasingly valued,and testing whether workers wear safety helmets has become an important measure to ensure the safety of construction sites.However,there are also significant challenges in the detection of safety helmets,such as changes in target size and complex background interference.This article proposes a safety helmet wearing detection method based on YOLOv8,which improves the network's feature extraction ability by introducing dilated convolution and convolutional attention mechanism,and updates parameters by combining localization loss function and confidence loss function.The experimental data shows that the accuracy of this method has been improved compared to the original YOLOv8,and it can accurately detect whether employees are wearing safety helmets.
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