Detection algorithm of safety helmet wearing based on deep learning

Abstract

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.

Applications of Computer Vision in Monitoring the Unsafe Behavior of Construction Workers: Current Status and Challenges

“…For classification tasks, the entire network is only comprised of convolutional layers, and the input image passes through the network only once. This means that the detection speed is fast, which perfectly meets the real-time requirements of production practices [46]. Li et al [49] proposed a CNN-based SSD-MobileNet algorithm to detect whether workers are wearing helmets or not.…”

“…With the continuous development of computer technologies, the use of target detection technology that relies on deep learning is becoming more and more popular. It can be divided into two categories, including two-stage detection methods based on candidate regions and one-stage detection methods based on regression [36,46]. The two-stage methods include R-CNN, Fast-R-CNN, Faster-R-CNN and other detection methods.…”

“…Li et al [49] proposed a CNN-based SSD-MobileNet algorithm to detect whether workers are wearing helmets or not. Huang et al [46] used the YOLO v3 algorithm to deal with the helmet-wearing problem of construction workers. Table 1 summarizes the research on PPE-use identification in workers.…”

Multiscale generative adversarial network for real‐world super‐resolution

“…Convolutional neural networks 8,9 have achieved good results in target classification 10 and recognition 11–13 . Inspired by it, many SR models based on convolutional neural networks have achieved powerful performance.…”

Towards the steel plate defect detection: Multidimensional feature information extraction and fusion

“…The fusion of multidimensional features 64,65 . Different features extracted from the same level are fused into new feature vectors, and then the features of different levels are associated and mapped.…”