## 1 Introduction
[14] used eight bands in MODIS data to perform multi-channel thresholding and extract smoke pixels. On the basis of spatiotemporal fluctuations in flame data, [PERSON] and [PERSON] [15; 16] presented an algorithm for detecting flames, which uses color information for smoke detection and has achieved good results. [PERSON] et al. [17] designed a random forest classifier for smoke detection and used a spatiotemporal bag-of-features histogram to construct a random forest classifier in the training phase, which improved the detection accuracy. [PERSON] and [PERSON] [18] extracted smoke edge features using the pyramid decomposition algorithm, and proposed a method for training and detecting smoke with a support vector machine. Although these traditional smoke detection methods have achieved good detection accuracy in experiments, several problems remain, such as low detection efficiency, the inability to process massive amounts of data automatically, the dependence on prior knowledge, and the manual extraction of feature information. Thus, these traditional smoke detection methods will not become popular or widely applied [19]. As deep learning has developed in recent years, there have been major advances in computer vision. These have effectively improved the efficiency and accuracy of target detection, and increased the popularity and application of smoke detection methods. In 2016, the one-stage object detection network You Only Look Once version 1 (YOLOv1) was proposed by [PERSON] et al. [20]. The detection speed of YOLOv1 is very fast, reaching 25 ms per image. Thus, it can be applied to real-time detection. Moreover, it can easily be migrated to other fields. However, YOLOv1 is not ideal for detecting small targets. Due to its loss function, the accuracy in positioning the target is not high [21]. Thus, [PERSON] and [PERSON] proposed YOLOv2, which improved the recall and positioning accuracy of the YOLOv1 network [22]. YOLOv2 added an anchor box to YOLOv1. It uses \(K\)-means clustering to calculate the anchor box size, which improves the recall rate of the algorithm. [PERSON] et al. developed YOLOv4 to detect marine organisms in a shallow sea [23]. [PERSON] et al. [24] proposed identifying pine trees abnormally discolored by a nematode infection with the YOLO algorithm using images taken by unmanned aerial vehicles. A smoke detection model based on a combination of an improved traditional optical flow and YOLOv3 was designed by [PERSON] et al. [25]. The detection accuracy of these algorithms is more than 73%, and the recognition tasks are completed accurately and quickly. With these properties, YOLO series algorithms are widely used in image recognition, video detection, and other target detection applications. The latest algorithm in the YOLO series is YOLOv5, which is an outstanding one-stage detection model in terms of speed and accuracy. It is an optimized version of YOLOv4. It not only has the advantages of previous versions and other networks, but it also changes the characteristics of the earlier YOLO algorithms, which had a fast detection speed but low accuracy. Moreover, it is more accurate when detecting small targets [26]. YOLOv5s is the fastest model with the smallest network depth and width among the four versions of YOLOv5. In this study, YOLOv5s was used to identify smoke from Sentinel-2 remote sensing images. Sentinel-2 is a component of the Copernicus program, operated by the European Space Agency (ESA). The Sentinel-2 Multispectral Imager (MSI) was carried by two satellites (Sentinel-2A and 2B), which can provide 13-band multi-spectral images at spatial resolutions of 10, 20, or 60 m with a 5-day revisit time. Therefore, Sentinel-2 remote sensing images have broad application prospects, including detection of smoke from crop residue burning. Although the original YOLOv5s model has better detection speed than other target detection algorithms, its detection accuracy is poor.