# YOLO-DFAN: Effective High-Altitude Safety Belt Detection Network
###### Abstract  
This paper proposes the You Only Look Once (YOLO) dependency fusing attention network (DFAN) detection algorithm, improved based on the lightweight network YOLOv4-tiny. It combines the advantages of fast speed of traditional lightweight networks and high precision of traditional heavyweight networks, so it is very suitable for the real-time detection of high-altitude safety belts in embedded equipment. In response to the difficulty of extracting the features of an object with a low effective pixel ratio--which is an object with a low ratio of actual area to detection anchor area in the YOLOv4-tiny network--we make three major improvements to the baseline network: The first improvement is introducing the atrous spatial pyramid pooling network after CSPDarkNet-tiny extracts features. The second is to propose the DFAN, while the third is to introduce the path aggregation network (PANet) to replace the feature pyramid network (FPN) of the original network and fuse it with the DFAN. According to the experimental results in the high-altitude safety belt dataset, YOLO-DFAN improves the accuracy by 5.13% compared with the original network, and its detection speed meets the real-time demand. The algorithm also exhibits a good improvement on the Pascal voc07+12 dataset. high-altitude safety belt detection; low-effective-pixel-ratio object; improved YOLOv4-tiny; attention mechanism +
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a high-altitude safety belt. [PERSON] et al. proposed another example: a training network model using the one-stage detection network YOLOv3, which aims to detect whether the worker in the input image is wearing a high-altitude safety belt [14]. Although some of these networks have achieved good results in high-altitude safety belt detection, the networks are large, and their hardware demand is too high when the actual application is deployed, hindering the high-altitude safety belt detection task, which imposes high demands for lightweight and real-time networks in the modern era. From the perspectives of detection speed and practical application, detection based on a lightweight network is more applicable for high-altitude safety belt detection. As an object with a low effective pixel ratio, high-altitude safety belt refers to a class of objects with a low ratio of actual area to detection anchor area, characterized by scattered distribution in the image, unfocused features, and easy confusion with the background. Therefore, it is difficult for the detection network to extract their effective features. Moreover, the human body only occupies part of the input image, and the high-altitude safety belt only covers a part of the human body. These problems have led to difficulty extracting the features of high-altitude safety belts using traditional detection networks. This paper proposes the YOLO dependency fusing attention network (DFAN) detection algorithm based on the YOLOv4-tiny detection algorithm to solve the major problems of detection speed and high-altitude safety belt feature extraction. YOLOv4-tiny is a lightweight network with an extremely fast detection speed, but it does not do a good job of detecting high-altitude safety belts; thus, we propose YOLO-DFAN. After features are extracted by the lightweight backbone network CSPDarkNet-tiny of YOLOv4-tiny, YOLO-DFAN introduces an atrous spatial pyramid pooling network (ASPPNet) [15] to capture contextual information and fuse it on multiple scales. This method increases the receptive field of the features while enriching the extracted high-altitude safety belt spatial feature information and obtaining a more refined feature representation of the network. Afterward, the idea of an attention mechanism is introduced [16]. The DFAN is innovatively proposed to optimize the representation of the feature layer in multiple dimensions by encoding the long-range dependency information for three directions to focus adequate network attention on the features of the high-altitude safety belt region.