###### Abstract
Although two-stage object detection has high detection accuracy, it does not perform well in real time. In this regard, target detection technology usually uses the one-stage target detection algorithm. The one-stage target detection algorithm can achieve real-time detection, and the detection accuracy can maintain the same level as the two-stage target detection algorithm [12]. The you only look once (YOLO) algorithm is an object detection algorithm that divides the trained image into a grid system. Each unit in the grid is responsible for detecting its own internal objects. The YOLO algorithm has occupied an important position in the field of target detection, with its excellent detection speed and accuracy, since it was first proposed in 2016. The YOLOv1 [13] algorithm regards the target detection problem as a regression problem, which is an end-to-end method with fast detection speed and good real-time performance. However, the detection accuracy of the algorithm is low, and it is difficult to detect when the target object is small. In order to improve this problem, [PERSON] et al. improved YOLOv1 by introducing batch normalization and dimension clustering to improve the detection accuracy and called the algorithm YOLOv2 [14]. On the basis of YOLOv2, [PERSON] et al. further improved it through a series of improvements, such as using a residual network to improve the network structure to achieve multi-scale output, thereby improving the accuracy of detection, and named the improved algorithm YOLOv3 [15]. Since the accuracy rate has been greatly improved after using the YOLOv3 algorithm, it has become one of the most used algorithms. [PERSON] et al. proposed the YOLOv4 algorithm [16] for some shortcomings in the YOLOv3 algorithm and made a series of improvements. The algorithm uses the cross-stage partial darknet-53 (CSP-Darknet53) [17] structure to optimize the network structure and uses the data enhancement method in the training phase to further improve the training speed and accuracy. In 2020, Ultralytics developed an open-source version. The backbone network of the YOLOv5 algorithm [18] takes into account both the detection efficiency and image recognition effect. The volume of the algorithm model can be adjusted, and the recognition result is more accurate than other detection methods, but the calculation amount of the model is large, and the structure is redundant. In response to these challenges, this paper proposes an improved YOLOv5-Lite target detection algorithm that combines multi-scale attention and bounding box regression, aiming to further improve the detection performance while maintaining the lightweight characteristics of the algorithm. As a widely used robot software platform, ROS1 provides a wealth of tools and libraries to support the development and integration of algorithms. The improved YOLOv5-Lite algorithm is integrated with ROS, which can not only realize the rapid deployment of the algorithm but also facilitate the interaction with other robot perception and decision-making modules through the modular characteristics of ROS. Next, the design and implementation of the improved YOLOv5-Lite algorithm will be introduced in detail, as well as the experimental process and result analysis in the ROS environment.