Construction worker hardhat-wearing detection based on an improved BiFPN

Work in the construction site is considered to be one of the occupations with the highest safety risk factor. Therefore, safety plays an important role in construction site. One of the most fundamental safety rules in construction site is to wear a hardhat. To strengthen the safety of the construction site, most of the current methods use multi-stage method for hardhat-wearing detection. These methods have limitations in terms of adaptability and generalizability. In this paper, we propose a one-stage object detection method based on convolutional neural network. We present a multi-scale strategy that selects the high-resolution feature maps of DarkNet-53 to effectively identify small-scale hardhats. In addition, we propose an improved weighted bi-directional feature pyramid network (BiFPN), which could fuse more semantic features from more scales. The proposed method can not only detect hardhat-wearing, but also identify the color of the hardhat. Experimental results show that the proposed method achieves a mAP of 87.04 degrees A, which outperforms several state-of-the-art methods on a public dataset.

Automated summary

Work in construction sites is considered high-risk, and safety is crucial. A one-stage object detection method based on convolutional neural network was proposed, which effectively identifies small-scale hardhats and achieves higher mAP through an improved approach.