## Abstract  
In practical applications, the intelligence of wheeled mobile robots is the trend of future development. Object detection for wheeled mobile robots requires not only the recognition of complex surroundings, but also the deployment of algorithms on resource-limited devices. However, the current state of basic vision technology is insufficient to meet demand. Based on this practical problem, in order to balance detection accuracy and detection efficiency, we propose an object detection algorithm based on a combination of improved YOLOv4 and improved GhostNet in this paper. Firstly, the backbone feature extraction network of original YOLOv4 is replaced with the trimmed GhostNet network. Secondly, enhanced feature extraction network in the YOLOv4, ordinary convolution is supplanted with a combination of depth-separable and ordinary convolution. Finally, the hyperparameter optimization was carried out. The experimental results show that the improved YOLOv4 network proposed in this paper has better object detection performance. Specifically, the precision, recall, F1, mAP (0.5) values, and mAP (0.75) values are 88.89%, 87.12%, 88.80%, 86.84%, and 50.91%, respectively. Although the mAP (0.5) value is only 2.23% less than the original YOLOv4, it is higher than YOLOv4, tiny, Efficientdet-d0, YOLOv5n, and YOLOv5 compared to 29.34%, 28.99%, 20.36%, and 18.64%, respectively. In addition, it outperformed YOLOv4 in terms of mAP (0.75) value and precision, and its model size is only 42.5 MB, a reduction of 82.58% when compared to YOLOv4's model size. W 2022 2022 [EMAIL_ADDRESS]. in 2015. Similar to the SPP layer, the fast R-CNN pools a RoI (region of interest) layer. For synchronization training, SPP adds the classification and bounding box regression phases to a deep network. While the fast R-CNN increases speed and accuracy, it also requires the use of external algorithms to extract target candidate boxes. Immediately after the fast R-CNN was proposed, [PERSON] et al. proposed the faster R-CNN model [11] which combines the process of extracting an object candidate box into a deep network. The faster R-CNN model is the first deep learning object identification method that is really end-to-end, as well as the first deep learning object detection algorithm that is quasi-real-time. The design of a regional candidate network is R-CNN's significant innovation (RPN). In 2016, [PERSON] et al. introduced the R-FCN (region-based fully convolutional network) model [12], where location-sensitive RoI pooling was added to improve object detection accuracy. They are all detection methods based on candidate areas in object detection, from R-CNN to R-FCN. In most cases, there are two steps to the implementation. The two-stage target identification approach involves extracting deep characteristics from an image and calculating candidate regions, followed by locating each candidate region (including classification and regression). Despite its excellent detecting accuracy, there is still a delay between speed and real-time. The researchers proposed a one-stage object detection technique to enable object detection to satisfy real-time requirements. The approach of \"coarse detection + refinement\" is abandoned and replaced by the method of \"anchor point + correction\" (this method only performs one feedforward network calculation, which is very fast and can achieve real-time effects). [PERSON] and [PERSON] et al. introduced the first one-stage object detection algorithm called YOLO [13] (you only look once) in 2015. The introduction of YOLO has revealed fresh information on the speed, which can now approach 45 frames per second. However, the limitations are clear, namely, demonstrating the limited number of network energy detection targets and the poor small object detection effect. [PERSON] et al. proposed the SSD [14] method in the same year. SSD incorporates YOLO's quick detection concept, merges RPN's advantages in faster R-CNN, and improves multi-size target processing.