# Automatic Detection of Construction Workers' Helmet Wear Based on Lightweight Deep Learning
## 2 Related Work
(2016) [17] tried an artificial neural network (ANN) combined with statistical features (GLCM) to detect the helmets from low-resolution image data. The algorithm comprised a local binary pattern (LBP) and gray-level co-occurrence matrix (GLCM) aided with a back-propagation artificial neural network. GLCM is an image descriptor that characterizes the image. It was observed that ANNs could be trained by experience when applied to new unknown input data. Following the footsteps of ANN models, CNN modeling came into existence. CNN was a groundbreaking aspect of deep learning, gaining popularity in recent years. The CNN was improved, and many new sub-CNNs were developed to enhance the efficiency. [PERSON] et al. (2018) [18] used the Faster R-CNN method to detect non-helmet-wearing workers. Faster R-CNN has a moderate processing time, but it has a higher precision when compared with old methods. Faster R-CNN is a two-stage algorithm, so when the Faster R-CNN is compared to competitive algorithms, the speed is lower than those methods. Therefore, a compromise should be made for speed and accuracy as Faster R-CNN is slow but highly accurate. On the subject of speed, YOLO, a new algorithm, was developed to overcome the speed issues of Faster R-CNN. [PERSON] et al. (2019) [19] administered YOLOv3 with Dark-Net-53 (backbone) feature detection algorithm for detecting unsafe actions conducted by workers on-site. The YOLO algorithm can easily be adapted to real-time surveillance systems to detect the helmet quickly. According to the data from this study, the accuracy was 96.6%. The good idea behind YOLO is that it is a one-stage algorithm. It can solve the regression directly but fail to generate a bounding box around RoI. Similarly, [PERSON] et al. (2019) [20] used improved YOLO v3 with a Dense-Net backbone to identify whether or not workers wear a safety harness while working. Upon implementation, the YOLO-Dense-Net backbone can also detect occluded objects well. YOLO has many versions and can be modified into new algorithms. The version used in the above research was a modified YOLO v3, which was 2.44% better than the original YOLO v3. The Single Shot MultiBox Detector (SSD) algorithm is worth mentioning when discussing one-stage algorithms. SSD's success is its ability to withstand different sizes and multi-scale feature maps. Additionally, the architecture is simple and efficient. Due to SSD's simplicity and frequent use, [PERSON] et al. (2019) [21] adopted this algorithm to build a safety head protection detection model. The attractive part of SSD is that it uses less GPU and performs better than previously mentioned methods. In addition, the proposed method can achieve a real-time speed of up to 21 fps. A comparative study was conducted by [PERSON] et al. (2020) [22]. The author built and compared three models using YOLO v3 architecture to check the speed and efficiency of all proposed models. The first model initially detects the worker and helmet, and then the information is passed to a machine learning classifier. The second model simultaneously detects workers with helmets in a single CNN framework. The third model uses cropped images, and then the CNN classifier classifies the images (as a helmet or non-helmet user). It was found that the second approach achieved the best results with 72% accuracy and could process 11 fps data. The third approach was second-best performing and gained an accuracy of 68%. However, the first model was the fastest and could process 13 fps with 63% accuracy. With the update of the YOLO series, [PERSON] and [PERSON] (2022) [23] proposed an automatic safety helmet detection system based on YOLOv5x with good detection capability for smaller objects and in terms of objects in low-light images. Recently, [PERSON] et al. (2020) [24] fabricated a model based on lightweight CNN to detect helmets in real-time. A depth-wise separable MobileNet model was used for detection.