# A Real-Time Safety Helmet Wearing Detection Approach Based on CSYOLOv3
## 1 Introduction  
With the expeditious evolvement of the construction industry in recent years, engineering construction projects can be seen everywhere across a city. The environment of a construction site is becoming more complicated and risky than ever before, and accidents happen frequently under some severe construction environments. Wearing a safety helmet when entering the construction site is a necessary protection measure for everyone, especially the workers who are more likely to be injured during the working process. However, with the increased difficulty of personnel management by humans, it is easy to cause safety accidents to the workers who are not wearing helmets or have non-standard operations. With more and more attention attached to site safety, many research works have been done in the area of object detection using video surveillance with regard to large-scale construction places [1; 2]. For example, [PERSON] et al. [3] put forward an ingenious and pragmatic safety helmet wearing (SHW) detection modus on the basis of image preprocessing and machine learning. The accuracy of detection for the newly raised method is up to 80.7% and the frame rate is 7 fps. [PERSON] et al. [4]presented a helmet detection method for two-wheeler riders with the assistance of machine learning and a user interface was provided to pay challans. This technology firstly captures the real-time image of road traffic and detect the two wheelers among all vehicles on the road, secondly it processed to recognize whether riders are wearing helmet or not. [PERSON] et al. [5] presented a system which is able to identify autobike riders and check if they were wearing safety helmets automatically at the same time. Futhermore, K-Nearest Neighbor(KNN) is adopted to make the classification between moving autocycles and other background objects on the road according to the features extracted from district attributes. The experimental data reveal that the average detection precision for various categories of lanes are all above 60% respectively. While the above-mentioned methods meet the requirements in terms of accuracy, they are not suitable for the application of real-time detection. Besides, these methods used for SHW detection are all in the light of traditional machine learning methods. The features used for safety helmet detection are artificially selected and designed, and the obtained feature are not robust enough. Consequently, target detection on the basis of deep learning is developping rapidly and applied extensively in many fields. In 2014, the fast Region-CNN(RCNN) algorithm based on Visual Geometry Group Network16(VGG16) [6] is practically 9 times as fast as RCNN in respect of training speed, about 3 times as fast as that of SPP-net, while test speed is 213 times faster than RCNN, 10 times as fasts as SPP-net. The mAP on Visual Object Classes 2012(VOC2012)is about 66%. Faster RCNN brought forward a fresh model architecture of RPN so that the full image convolutional features can be utilized together with the detection model, which has a frame rate of 5 fps on a GPU and achieves the detection accuracy of 73.25% mAP on PASCAL VOC2007. Currently, with the rapid development of artificial intelligence, target detection and recognition algorithm based on deep learning has attracted much attention for researchers both at home and abroad [7; 8; 9; 10; 11; 12; 13; 14]. For instance, [PERSON] et al. [15] presented a fresh method named SSD for target detection in pictures with applying a single deep neural network of ECCV2016. The multistage feature maps are employed as the basis of classification and regression, achieving the multi-scale effect [PERSON] et al. [16] raised a novel detection network model called R-CNN which is simple and scalable, achieving a mAP of 53.3% in 2014. Neural network migration learning method is proposed in this paper when lacking of labeled data, adopting neural network trained in other large datasets, and then fine tune in small-scale specific datasets.