## 1 Introduction  
As the most flexible part of human body, the use of hands for communication is highly expressive, and the use of gestures can be seen in various interaction scenarios [1; 2; 3]. During interaction, machines need to correctly understand human gestures and react accordingly [4]. This interaction should be natural and metaphorical, allowing for more natural and intuitive communication with various systems [5; 6]. Machine vision-based gesture recognition, however, does not require physical contact with the user, nor the use of additional sensors or specialized equipment. Instead, it only needs to be trained with a large amount of image data to obtain a highly accurate recognition model and achieve accurate gesture recognition [7; 8; 9; 10]. Early gesture recognition methods relied on manually extracting gesture features (e.g., shape, color, texture, etc.) for gesture recognition [11; 12; 13; 14], but such methods relied on expert knowledge and could not solve the problem of complex images with skin-like backgrounds [15]. With the development of computer and deep learning techniques, many scholars have used advanced deep learning models for gesture recognition [16; 17; 18; 19; 20]. Compared with early gesture recognition methods, using deep learning methods for gesture recognition has the characteristics self-extraction of features, strong adaptability and robustness, high accuracy, and greater efficiency to handle large-scale data [21; 22; 23]. However, when using deep learning methods for gesture recognition, the recognition accuracy will be significantly decreased if there are complex background interferences, such as skin colors, in the gesture image [24]. With the development of object detection algorithms, represented by SSD (single-shot detector) [25] and YOLO (you only look once) series algorithms [26; 27; 28; 29], many scholars have transformed the gesture classification problem into a gesture object detection problem. According to whetherthere is an explicit candidate region extraction stage during gesture detection, gesture detection algorithms can be divided into one-stage and two-stage algorithms [30]. One-stage gesture detection algorithms directly predict the position and classes of the gesture from the input image without an explicit candidate region extraction stage, which requires less computation and is faster. The two-stage gesture detection algorithms usually consists of a gesture candidate region extraction stage and a gesture classification stage. In the gesture candidate region extraction stage, some techniques (e.g., Selective Search [31] and RPN [32], etc.) are used to generate candidate regions, which are possible regions containing targets. Since explicit candidate region extraction is required, the algorithm has a higher computation cost and slower speed. Despite the improvement in recognition accuracy for gesture images with complex backgrounds, detecting and recognizing gestures using object detection algorithms still cannot provide a good trade-off between recognition speed and accuracy. To address these issues, this paper proposes a gesture recognition method based on improved YOLOv5I for complex backgrounds using the one-stage object detection network YOLOv5 as the benchmark network. An efficient layer aggregation network is also used to enhance the network's feature learning and expression ability while further improving its recognition speed. Moreover, we introduce the CBAM attention mechanism module to enhance the network's robustness to complex backgrounds. The main contributions of this paper are as follows:  
* A gesture recognition algorithm based on improved YOLOv5 is proposed for complex background scenarios. * By using an efficient layer aggregation network module, the benchmark network YOLOv5 is enhanced for complex background gesture feature extraction. It can also effectively reduce model complexity and improve network recognition speed.