# Multi-Scale Safety Helmet Detection Based on SAS-YOLOv3-Tiny
###### Abstract
[PERSON] et al. developed a real-time detection algorithm called YOLO [9], and its entire framework is a single network, which omits the proposal generation step and can be optimized end-to-end. In 2016, SSD [10] was proposed to solve the limitations of YOLO. In the one-stage algorithm, the imbalance between foreground and background is a serious problem because there is no proposal generation to filter out easily generated negative samples, [PERSON] et al. proposed RetinaNet [11] to solve the class imbalance problem in a more flexible way. [PERSON] et al. proposed an improved version of YOLO, YOLOv2 [12], which significantly improved the detection performance and maintained the real-time reasoning speed. Later, [PERSON] et al. proposed YOLOv3 [13], which used the Darknet-53 network structure and the idea of the residual network for reference. In addition, the idea of FPN was used to carry out multi-scale feature detection. The above improvements made YOLOv3 three times faster than SSD, while its accuracy is the same as SSD. Compared with the anchor-based algorithm, the anchor-free algorithms do not depend on the pre-defined anchors and avoid the complicated calculations related to the anchors. The earliest anchor-free methods are the Unifying Landmark Localization with End to End Object Detection (Densebox) [14] and YOLO [9]. The following anchor-free methods [15; 16; 17] are detection methods based on keypoints, and compared with YOLO and Densebox, the detection effect of these three methods is significantly improved. Finally, three methods [18; 19; 20] belong to the intensive prediction method, and they all obtain the desired result by directly predicting the rectangular box without using the anchor. In recent years, more and more object detection algorithms are applied in the security field. [PERSON] et al. put forward a real-time safety helmet wearing detection approach (named CSYOLOV3) [21], it achieved the mAP value of 67.05%, and its FPS reached above 25, so the mAP value was low and the speed was slow. [PERSON] et al. proposed a deep learning-based safety helmet detection in engineering management based on convolutional neural networks [22], which would have a deficient performance when the images are not very clear, such as the safety helmets being too small and obscure. In addition, the above works only divide the categories of objects into two categories: Wear and Nowwear, but the types of objects on the head are not distinguished. It is necessary to distinguish between different types of objects because ordinary hats are not safe, and different kinds of helmets have different protective effects on the head. Experimental studies have found that the general object detection algorithms can be applied to the detection task of the safety helmet. However, under complex scenarios, the small-scale object is sheltered and it is dense. The remote small-scale safety helmets and hats with low resolution and blurry pixels have less characteristic information, which leads to the phenomenon of missed detection. In addition, it is challenging to balance the accuracy and complexity in general object detection algorithms, and the imbalance between the two makes it difficult to deploy on mobile devices. Even though YOLOv3 is a widely used object detection algorithm with good recognition speed and detection accuracy by combining several methods such as residual network, feature pyramid and multi-feature fusion network, it has lots of parameters and amount of computation and generates a large model. Hence, it is challenging that the model is transplanted to embedded applications when computing power and storage space are limited. YOLOv3-tiny based on YOLOv3 is a lightweight object detection network applying an embedded platform, but its detection accuracy is low. In this paper, SAS-YOLOv3-tiny is proposed to balance the detection accuracy and speed for a set of self-built helmet datasets.