PDF(Pubmed)
Abstract：
In the field of industrial safety, wearing helmets plays a vital role in ensuring workers\' health. Aiming at addressing the complex background in the industrial environment, caused by differences in distance, the helmet small target wearing detection methods for misdetection and omission detection problems are needed. An improved YOLOv8 safety helmet wearing detection network is proposed to enhance the capture of details, improve multiscale feature processing and improve the accuracy of small target detection by introducing Dilation-wise residual attention module, atrous spatial pyramid pooling and normalized Wasserstein distance loss function. Experiments were conducted on the SHWD dataset, and the results showed that the mAP of the improved network improved to 92.0%, which exceeded that of the traditional target detection network in terms of accuracy, recall, and other key metrics. These findings further improved the detection of helmet wearing in complex environments and greatly enhanced the accuracy of detection.
摘要：
在工业安全领域，戴头盔对确保工人的健康起着至关重要的作用。针对工业环境中的复杂背景,由于距离的差异，头盔小目标佩戴检测方法需要针对误检和漏检问题进行检测。提出了一种改进的YOLOv8安全帽佩戴检测网络,以增强细节捕获,改进多尺度特征处理,通过引入扩展残差注意模块提高小目标检测的精度,atrous空间金字塔池化和归一化Wasserstein距离损失函数。在SHWD数据集上进行了实验，结果表明，改进后的网络的mAP提高到92.0%，在准确性方面超过了传统的目标检测网络，召回，和其他关键指标。这些发现进一步改善了复杂环境下头盔佩戴的检测，并大大提高了检测的准确性。
