Detection algorithm of safety helmet wearing based on deep learning

In the production and construction of industry, safety accidents caused by unsafe behaviors of staff often occur. In a complex construction site scene, due to improper operations by personnel, huge safety risks will be buried in the entire production process. The use of deep learning algorithms to replace manual monitoring of site safety regulations is a powerful guarantee for sticking to the line of safety in production. First, the improved YOLO v3 algorithm is used to output the predicted anchor box of the target object, and then pixel feature statistics are performed on the anchor box, and the weight coefficients are respectively multiplied to output the confidence of the standard wearing of the helmet in each predicted anchor box area, according to the empirical threshold determine whether workers meet the standards for wearing helmets. Experimental results show that the helmet wearing detection algorithm based on deep learning in this paper increases the feature map scale, optimizes the prior dimensional algorithm of specific helmet dataset, and improves the loss function, and then combines image processing pixel feature statistics to accurately detect whether the helmet is worn by the standard. The final result is that mAP reaches 93.1% and FPS reaches 55 f/s. In the helmet recognition task, compared to the original YOLO v3 algorithm, mAP is increased by 3.5% and FPS is increased by 3 f/s. It shows that the improved detection algorithm has a better effect on the detection speed and accuracy of the helmet detection task.

Automated summary

This paper utilizes deep learning algorithms for helmet wearing detection, increasing feature map scale, optimizing prior dimensional algorithm for specific helmet dataset, improving loss function, and combining image processing pixel feature statistics for accurate detection of helmet wearing. Experimental results show that the algorithm achieves an mAP of 93.1% and FPS of 55 f/s, with a 3.5% increase in mAP and 3 f/s increase in FPS compared to the original YOLO v3 algorithm, indicating better detection speed and accuracy for helmet recognition task.