# Detection of Safety Signs Using Computer Vision Based on Deep Learning
## 1 Introduction  
With the progress of artificial intelligence (AI) technology, computer vision (CV) has made great achievements and tends to be widely used in the coal mine safety field. Recent advances in CV could help effectively solve coal mine safety problems, such as fire detection [1], safety protection equipment detection [2], and early warning systems [3]. However, research on the development of CV technologies for coal mine safety issues is still very rare [4; 5; 6; 7; 8; 9]. Object detection is an important topic in the CV field, which can serve as an effective tool for, e.g., AI-based safety supervision and early warning systems; for example, the detection of safety signs and hazardous materials could help to automatically supervise unsafe behaviors and hazardous materials in coal mines. A variety of image detection approaches have been proposed based on image color, shape, and machine learning. Machine learning algorithms aim to mine hidden rules from large amounts of data and use them for prediction or classification [10]. Deep learning is an important branch of machine learning. It is a new intelligent algorithm of supervised learning that has been proposed in recent years [11; 12; 13]. Its deep network has strong learning ability and can learn deeper features. Among many deep neural networks, convolutionalneural network (CNN) is the first deep learning technology applied in the field of image recognition, which replaces the traditional artificial feature extraction and greatly improves the accuracy of recognition. Therefore, CNN facilitates deep learning for the recognition and detection of images. Most of the current research in object detection focuses on traffic sign recognition [14; 15], medical image detection [16], and intelligent agriculture [17]. [PERSON] et al. [18] used the Faster-RCNN method to detect traffic signs. The experimental results show that Faster-RCNN can indeed be applied to this field. [PERSON] et al. [19] used CNN to distinguish normal people from people affected by viral pneumonia, which showed CV can achieve effective detection in medicine. [PERSON] et al. [20] proposed an algorithm based on Faster-RCNN to detect small pests on images with different pest densities and light reflections, providing technical references for pest monitoring and population estimation. The literature review reveals that while target detection can identify and locate objects, its application within computer vision remains constrained. A significant shortfall exists in the domain of safety sign detection, with a notable absence of algorithmic models tailored to this specific need in current research. The development of models capable of classifying and accurately locating various types of safety signs would establish a critical foundation. Such advancements could facilitate future endeavors to detect the use of helmets, protective masks, and gloves among workers, prompting adherence to relevant rules and regulations. Implementing these measures would enhance safety protocols and more effectively mitigate the incidence of workplace accidents. This paper aims to demonstrate the applicability and improve the performance (detection speed and accuracy) of object detection in the coal mines' safety science field, focusing on the detection of safety signs due to carrying out safety restraints, regulations, and rules. The main contributions of this paper are summarized as follows:  
1. The safety sign image dataset contained 2000 images with 10 categories: wearing protective gloves, wearing a safety helmet, wearing electric shock, warning electric shock, warning poisoning, emergency exit, emergency shelter, no climbing, no smoking, and no fireworks. 2. Attention mechanisms were introduced to make the network focus on important information and reduce the influence of useless information. 3. The Soft-Non Maximum Suppression (Soft-NMS) algorithm was used to replace the traditional NMS algorithm so that more correct prediction boxes can be retained and thus further optimize the detection model. 4.