## Abstract
Most previous methods create predictions using semantically rich high-level convolutional features; however, the high-level features will result in the loss of some detailed information, and the target location will be unclear. [PERSON] et al. proposed the FPN [15] (feature pyramid networks) at the end of 2016. FPN improves the network processing capabilities for small targets by combining high-level, low-resolution, strong semantic information with low-level, high-resolution, weak semantic information. The FPN can be used in conjunction with either a one-stage or two-stage object detection approach. At the same time, YOLO released YOLOv2 [16], which added a batch normalization layer for each convolutional layer to increase convergence while removing the fully connected layer from the network. Although one-stage object detection is significantly faster than two-stage object detection, the detection accuracy is not as good as two-stage object detection. As a result, in 2017, [PERSON] proposed the RetinaNet [17] detection model. The focus loss function, proposed by RetinaNet, decreases the learning weight of simple background samples during network training. [PERSON] et al. proposed the RefineDet [18] in late 2017. The ARM (anchor refinement mould), the ODM (object detection module), and the TCB (transfer connection block) are all proposed by RefineDet. YOLOv3 [19] was updated in early 2018, replacing the softmax function with numerous independent classifiers for multi-dimensional prediction, comparable to the feature pyramid network. Even though both two-stage and one-stage object detection algorithms have made significant progress on high-performance devices, they need significant processing resources to run. As a result, these algorithms are challenging to implement on some resource-constrained devices. Some lightweight object detection algorithms, such as YOLOv3_tiny [20], YOLOv4_tiny [21], Mobilenetv1 [22], Mobilenetv2 [23], Mobilenetv3 [24], and others [25; 26; 27; 28; 29; 30] have been offered for this problem. However, the identification accuracy suffers as a result of the network model's compression, making it impossible to detect some complicated sceneries and small target items. With the continuous development of object detection technology and robotics, several object detection methods are deployed to robots and applied to various fields, for example. agriculture [31; 32; 33; 34; 35; 36], industry [37; 38; 39; 40], health [41], unmanned vehicles [42], sports [43; 44], and so on. The RoboMaster AI Challenge brings together deep neural network enthusiasts from all around the world to research robotics. It is also envisaged that the findings would be applied to field rescue, unmanned driving, autonomous logistics, and other industries in order to benefit human life. A wheeled mobile robot should be able to recognize, track, and shoot targets in the RoboMaster AI Challenge. The key to success is figuring out how to make the object detection algorithm on the mobile robot ensure detection accuracy. This paper takes this task as the specific application context for the study. The main contributions of our work can be summarized as follows:  
1. This study proposes an improved YOLOv4 object detection algorithm that replaces the YOLOv4 backbone extraction network with a trimmed GhostNet lightweight network. This algorithm improves performance over the YOLOv4-related algorithm while maximizing accuracy retention. 2. This study uses a combination of deep separable convolution and ordinary convolution to improve ordinary convolution, which significantly reduces the number of parameters and makes the improved network more lightweight and efficient, suitable for deployment on resource-constrained mobile robotic devices, with great potential for applications. 3. To demonstrate the feasibility of the algorithm, we experimentally validate the object detection using a wheeled movable robot in the context of DJI's RoboMaster AI challenge application. 4. The experimental dataset in this paper is from DJI.