###### Abstract  
This paper primarily investigates enhanced object detection techniques for indoor service mobile robots. Robot operating systems (ROS) supply rich sensor data, which boost the models' ability to generalize. However, the model's performance might be hindered by constraints in the processing power, memory capacity, and communication capabilities of robotic devices. To address these issues, this paper proposes an improved you only look once version 5 (YOLOv5)-Lite object detection algorithm based on efficient multi-scale attention and bounding box regression combined with ROS. The algorithm incorporates efficient multi-scale attention (EMA) into the traditional YOLOv5-Lite model and replaces the C3 module with a lightweight C3 Ghost module to reduce computation and model size during the convolution process. To enhance bounding box localization accuracy, modified precision-defined intersection over union (MPIoU) is employed to optimize the model, resulting in the ROS-YOLOv5-FleetEMA model. The results indicated that relative to the conventional YOLOv5-Lite model, the ROS-YOLOv5-FleetEMA model enhanced the mean average precision (mAP) by 2.7% post-training, reduced giga floating-point operations per second (GFLOPS) by 13.2%, and decreased the params by 15.1%. In light of these experimental findings, the model was incorporated into ROS, leading to the development of a ROS-based object detection platform that offers rapid and precise object detection capabilities. ROS; efficient multi-scale attention; C3 Ghost; MPDIoU; YOLOv5-Lite +
Footnote †: journal: Applied  
0000-0001-931 problem of target detection in computer vision [2]. At present, the commonly used object detection network based on deep learning can be roughly divided into the following two categories: one-stage and two-stage [3]. For two-stage object detection, [PERSON] et al. proposed the classical region-based convolutional neural network (R-CNN) [4] algorithm. Firstly, about 2000 region proposals are obtained using selective search, then the features of region proposals are extracted by AlexNet4 [5], and then these features are regressed by multiple classifiers. Subsequently, [PERSON] et al. proposed a spatial pyramid pooling network [6] (SPPNet), which extracts more feature information by performing convolution operations on the entire image to avoid the problem of computational redundancy when R-CNN extracts features for all candidate regions. Therefore, the fully connected neural network (F-CNN) [7] adds an SPPNet between the last convolutional layer and the fully connected layer to extract a fixed-length feature vector and avoid the normalization of the region proposal. [PERSON] et al. proposed fast R-CNN [8] by referring to SPPNet, which simplifies the SPP layer to the region of interest (ROI) layer and applies singular value decomposition (SVD) to the output of the fully connected layer to accelerate the test process. Fast R-CNN combines classification with a bounding box, but fast R-CNN has the problem of excessive calculation. In this regard, [PERSON] and others then proposed faster R-CNN [9], which uses a region proposal network (RPN) instead of a selective search algorithm to extract region proposal, which greatly improves the detection efficiency. On the basis of faster R-CNN, [PERSON] et al. proposed the feature pyramid network (FPN) [10], which uses RPN to extract candidate regions on the feature pyramid. By fusing deep and shallow feature information, prediction is performed at different scales to enhance the semantic understanding of shallow feature maps, thereby improving the accuracy of small target detection. In order to further improve the detection speed, [PERSON] et al. proposed a region-based fully convolutional network (R-FCN) [11], replacing the fully connected layer with a fully convolutional layer, allowing the features of each candidate region to perform convolution operations directly to obtain the confidence of each category.