# Improved Lightweight Multi-Target Recognition Model for Live Streaming Scenes
## 2 Related Work
### Application of Yolov5 Algorithm for Object Detection  
Traditionally, feature extraction in object detection heavily relied on manual feature design, which often resulted in poor generalization. However, with the emergence of deep learning, Convolutional Neural Networks (CNNs) have emerged as the mainstream framework for machine learning in object detection, thanks to their remarkable performance and excellent feature extraction capabilities, starting from the introduction of R-CNN. One-stage and two-stage algorithms are the two main types of deep learning-based object detection algorithms. Firstly, one-stage algorithms directly predict the object's coordinates and class through regression, offering faster recognition speeds. On the other hand, two-stage algorithms employ region generation for target classification and calibration, leading to higher accuracy. However, the two-stage approach comes with increased computational overhead, reducing the model's speed and hindering real-time monitoring [22; 23]. Since 2015, the YOLO (You Only Look Once) family of single-stage deep learning algorithms has undergone continuous improvements. YOLO utilizes a convolutional neural network architecture to determine the location and type of objects in an image, enabling high-speed recognition. The yolov5 deep learning algorithm further enhances efficiency by adopting a more lightweight network architecture, significantly reducing the weight and improving the speed. The yolov5 family comprises four different architectures (YOLOv5x, YOLOv5l, YOLOv5m, and YOLOv5s), allowing flexibility in adapting to various object detection requirements by adjusting the extracted features' width and depth [24].  
YOLOv5s, the lightest variant in the YOLOv5 series, boasts the fastest recognition speed and finds widespread application in various scenarios. [PERSON] et al. [25] utilized a YOLOv5s model with channel pruning to achieve remarkable results in fast apple fruit detection. [PERSON] et al. [26] optimized the backbone network of the YOLOv5s and integrated the SE attention mechanism, significantly improving the model's accuracy compared to YOLOv5s and YOLOv4. [PERSON] et al. [27] employed YOLOv5s in an industrial setting for forklift monitoring, enhancing the backbone section with the GhostNet and incorporating the SE attention mechanism. [PERSON] et al. [11] pioneered the application of YOLOv5 in a live streaming scenario for real-time monitoring of anchor expressions. The improved YOLOv5s model incorporates the GhostNet module and the CA attention mechanism, achieving a superior balance between precision and speed.  
The previous YOLOv5 model has found extensive applications in various commodity environments. However, as the live streaming scene is still a nascent industry, there is significant potential to explore more applications for YOLOv5 in this domain. While [PERSON] et al. [11] achieved effective recognition of anchor expressions through an improved model, our focus extends beyond expressions to encompass other elements within the live streaming scene. Therefore, the re-application of the model for further improvements becomes particularly crucial.