# Automatic Detection of Construction Workers' Helmet Wear Based on Lightweight Deep Learning
## 2 Related Work  
Traditionally, helmet detection is a tedious task. Before helmet testing, the person is usually tested. [PERSON] et al. (2011) [11] detected helmets using a Haar-like feature algorithm. The Haar-like feature first detects possible face regions. Then, above the face region, it detects the helmet using color detection. The AdaBoost learning (classifier) method was adopted during the face and non-face classification, which helps to remove outliers. Similarly, [PERSON] et al. (2013) [12] proposed a \"Hybrid descriptor for features Extraction\", a traditional machine learning approach employed to detect helmets of motorcycle riders. His algorithm was based on a local binary pattern (LBP), a histogram of oriented gradients, and a circular Hough transform descriptor aided with a hybrid descriptor. SVM and random-forest classifiers were used for vehicle classification. Later, workers were detected in live video streaming by an algorithm presented by [PERSON] et al. (2016) [13]. Their vision-based method facilitates monitoring construction site workers to detect the wearing of a helmet, per safety regulations requirements. Histogram of gradient (HoG) features, combined with pair-wise matching, was used to detect the workers with helmets. This method relies on background subtraction to speed up the detection of human bodies. The proposed method allows a helmet of any color to be easily detected. Similar to the previous work, [PERSON] et al. (2015) [14] developed a tool that automatically detects the workers who are not wearing helmets in real-time video. The procedure uses a Haar-like feature detection algorithm that detects the worker's face. Once the human face is detected, this information is sent to the helmet detection algorithm, which uses the edge detection algorithm (semi-circular appearance of helmet) combined with color detection, and detects the helmet on a worker's head. Another vision-based method by [PERSON] et al. (2015) [15] came to light from research. The authors used the traditional machine learning approach with an available dataset of true and false matches of helmet images. The method contains background subtraction followed by SVM, dilation, erosion, and rectangle fitting. The authors extended the HoG technique to detect helmets directly in their work. It can easily detect the helmet with different colors. The helmet is detected in the upper part of the rectangle, which encloses the human. For training data, the helmets were annotated manually. Almost the same technique was adopted by [PERSON] et al. (2016) [2] with a different dataset of images, though they only used HoG and circular Hough transform with SVM to train the data in their approach. [PERSON] et al. (2017) [16] used SURF features combined with cascaded filters (HoG, Haar-like, and LBP) to detect workers with hard hats. The SURF detector also performed very well. [PERSON] et al. (2017) [3] used the ViBe-Background modeling algorithm combined with HoG and SVM to detect helmeted workers in substations. The ViBe-Background algorithm extracts workers from complex backgrounds, and HoG + SVM detects whether workers are wearing helmets. The method achieves an accuracy of 89% when using test data. In all, traditional methods have performed very well in the past, but all methods have some inevitable problems. Most of the methods shared the same issues while detecting helmets. In the methods above, most methods used HoG, color segmentation, and SVM. These methods mostly fail to detect illumination changes, occlusions, color variations, non-circular objects, and busy backgrounds. The advantage of traditional methods is that they do not require a lot of data to train. However, the execution speed and accuracy are not high. To address the limitations of traditional methods, then there is a need to develop more robust helmet detection. Developing suitable CNN algorithms based on data from construction sites is a feasible solution to this problem. In such attempts, [PERSON] et al.