# Deep Learning-Based Automatic Safety Helmet Detection System for Construction Safety
## 4 Result and Discussion
In Figure 9a,b, almost all the possible scenarios (single object, very small object, multiple objects, object from the back) have been accurately identified, thereby showing the ability to detect small objects with higher confidence scores. Furthermore, existing studies have faced the problem of detecting safety helmets in low-light conditions and against complex backgrounds. On the contrary, the proposed YOLOv5x-based model showed significant results even in low-light conditions. Figure 10 presents the prediction results of YOLOv5x in low-light conditions. Therefore, the proposed model could potentially be implemented as a real-time safety helmet detection system at construction sites to ensure worker safety in cases of inevitable accidents. Figure 8: Training and Validation curve of loss for the YOLOv5x model. (**a**) Loss curve for bounding box, (**b**) Loss curve for class, (**c**) Loss curve for object. Figure 9: Prediction results of the YOLOv5x model. (**a**) Helmet class, (**b**) Head class. Despite the significantly better performance, the proposed helmet detection model has certain limitations. For example, the proposed model faces problems in scenarios such as where the helmet is behind an obstacle(s) or with objects identical to helmets. Figure 11 shows potential detection errors; Figure 11a,c illustrate the poor performance of the proposed model when the helmet is behind bars due to the inability of the model to detect the ground truth. Nevertheless, instead of missing all the objects, the model is able to partially see objects obfuscated by the bars. Similarly, Figure 11b represents a scenario where an object looks like a helmet, and due to low light and obstacle structure the model could not detect it as another object. These problems could potentially be addressed in future studies by proposing novel deep learning frameworks for the discussed scenarios. The proposed model is also compared with some state-of-the-art models. Table 4 shows the detailed comparison. [PERSON] et al. [20] and [PERSON] et al. [34] used SSD-based learning for safety helmet detection. Both studies have shown stable results, but they struggle to detect small-scale safety helmets. [PERSON] et al. [30] and [PERSON] et al. [32] used YOLO-based learning for personal protective equipment (safety helmet,  
Figure 11: Detection Errors by the YOLOv5x model. (**a**,**c**) Helmet with Obstacle, (**b**) Object Similar like Helmet. Figure 10: Prediction results of the YOLOv5x model in low-light conditions. construction sites. They have used relatively small datasets, which could lead to the poor generalization of the models. One of the most significant limitations of their work was that they struggled to detect small helmets in low-light conditions. [PERSON] et al. [35] used transfer learning based on DenseNet architecture and applied two strategies, feature extractor and fine-tunning, for safety helmet detection. Despite the excellent results shown in their study, it would not be feasible to use their method in real-time scenarios as it is based on face detection techniques. Therefore, their model cannot detect the workers with their back to the surveillance camera, and in real-time scenarios it is impossible to always capture the faces of all the workers. Our proposed study shows stable results in all the different scenarios. Figure 9 shows that the model can detect helmets even when workers have their back to the camera, and Figure 10 shows the model is very efficient in detecting smaller helmets even in low-light conditions.