# Improved Lightweight Multi-Target Recognition Model for Live Streaming Scenes
## 1 Introduction  
Live e-commerce has emerged as a prominent marketing trend, with its role as a powerful sales-boosting tool being widely embraced globally [1]. Since 2019, leading global retailers like Amazon and QVC have established their own live video shopping platforms. In particular, China has witnessed a significant surge in the user base of live e-commerce, reaching a staggering 469 million in 2022, indicating its immense commercial potential. The utilization of real-time marketing strategies in live streaming scenarios effectively conveys sensory cues to viewers, thereby stimulating consumer purchases [2]. Consequently, the ability to capture these sensory cues during live streaming has become increasingly crucial. In live streaming, the primary focus of consumers' visual attention is centered around the anchor and the commodity, as these factors play a crucial role in influencing their purchasing decisions. To extract such visual cues effectively, object detection algorithms in machine vision have proven to be invaluable. Machine vision, a mainstream field within deep learning, encompasses various subfields including scene recognition, object recognition, object detection, and video tracking [3]. Among these subfields, object detection models based on deep learning have undergone significant advancements since the occurrence of Region-based Convolutional Neural Networks (R-CNN), resulting in notable improvements in both accuracy and speed [4]. Traditional object detection techniques can be partitioned into two groups: single-stage and two-stage object detection. The former, such as RCNN, Fast RCNN, etc., are lightweight and offer fast processing speeds. Conversely, the other techniques achieve higher accuracy but require significant computationalresources. The YOLO algorithm, widely employed in practical applications, serves as an excellent example of a single-stage object detection technique that achieves comparable accuracy to two-stage methods [5]. The introduction of attention mechanisms into machine vision has been a great success. The attentional mechanism in general is a dynamic selection process, adaptively weighting the input features, resulting in significant performance and accuracy improvements in object recognition, but with a relatively larger computation. Attention mechanisms, such as Shuffle Attention (SA), Convolutional Block Attention Module (CBAM), and Coordinate Attention (CA), have been developed to achieve lightweight enhancements and can be easily integrated into mobile network modules [6]. In recent years, researchers have been actively exploring lightweight modules such as GhostNet, MobileNetV3, and BlazeFace [7; 8]. Additionally, many scholars have been attempting to refine the backbone section of YOLOv5 with lightweight modules and incorporate attention mechanisms, aiming to strike a balance between accuracy and computational efficiency. [PERSON] et al. [9] integrated the Squeeze and Excitation (SE) attention mechanism into YOLOv5 for tomato virus disease identification, achieving higher accuracy. However, this modification resulted in an increased inference time compared to the original model, and the attention mechanism consumed a significant amount of computational resources. [PERSON] et al. [10] substituted the YOLOv5 backbone network with ShuffleNetV2 and integrated the CA attention mechanism, achieving a favorable balance between the indicators for mask detection. [PERSON] et al. [11] enhanced the backbone of the YOLOv5 model using GhostNet and incorporated the CA attention mechanism to detect anchor expressions in live streaming scenarios, yielding promising results. In live streaming scenes, relying solely on facial expressions is insufficient to capture the rich visual cues. To address this, we advise utilizing a new dataset that contains both anchor facial expressions and commodities.