# Mobile Robot Tracking with Deep Learning Models under the Specific Environments
## 2 Literature Review
### YOLO v5  
The YOLO model was proposed by [PERSON] et al. [24] in 2015 for object detection. Later, the Ultralytics proposed YOLOv5 as the modified version, and it is still being updated by them [20,20,25,30,31]. The YOLOv5 target detection network has four versions, which are the YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x models. In our experiment, YOLOv5s with the smallest depths and widths in the YOLOv5 series are adopted to consider the restriction by hardware for the IoT application scenarios. The network structure of YOLOv5 is built according to the one-stage structure. It can be divided into four sections: Input, Backbone, Neck and Prediction. YOLOv5 takes advantage of the Mosaic method to realize data enhancement, which meets the image's arbitrary scaling, clipping and layout requirements from the bottom of the view. The adaptive anchor frame calculation is the initial length and width of various database anchor frames. The YOLO model has a low generalization rate in terms of the aspect ratio of objects which means it is unable to locate objects of unusual proportions. Meanwhile, the YOLO model's inaccurate positioning is also a significant problem.  
The parameter settings of YOLOv5 focus on two yaml files which are voc_car.yaml and YOLOv5s car.yaml in our experiment. The voc_car.yaml file's function is mainly to declare the type to be recognized and decide the location of the name, training data set and testing data set. The YOLOv5s car.yaml file contains depth_multiple, width_multiple, anchor and head. Depth_multiple controls the number of submodules. Width_multiple controls the number of convolution kernels. The anchor size is set to 640 \(\times\) 640. The size of the anchor frame under the image size realizes that large targets can be detected on the small feature map and small targets can also be detected on the large feature map. Heads in YOLOv5 include the two parts neck and detect_Head. The neck adopts the PANet mechanism. The structure of detecting is the same as that head in YOLOv3. Bottleneckcsp is set to false, which indicates that the residual structure is not used. It uses Conv in the backbone.  
In the process of model training, the network completes the output of the prediction framework according to the parameters of the initial anchor framework. The data of the predicted frame is extracted and compared with the real frame to reverse the data update cycle. During the process, the network parameters are iterated and optimized continuously. The adaptive picture zooming is an improved section of YOLOv5, which reduces the effect of black padding due to image scaling on model training. At the input of the experiment, the parameter of the size is 610 \(\times\) 610.  
From the view of the Backbone, focus structure and CSP structure are equipped and cooperate with each other to coordinate the processing of images. With the assistance of Focus, the image is sliced to construct the corresponding feature map. Meanwhile, the CSP structure particularly points to the CSP1_X structure. For the structure of Neck, YOLOv5 adopts FPN and PAN architecture, which improves the basic convolutional operation on the one hand and strengthens the ability of network feature fusion on the other hand. It is worth mentioning that CSP_X is equipped and used in this section.  
As discussed in [32], the three models of Faster RCNN, SSD and YOLO are popular for industrial applications. There was no perfect algorithm or model for all the applications. Careful model selection should be made to fit a typical application [31].  
This paper focuses on comparing these latest deep learning models for target tracking with interference and partial occlusion. We use a unified operating hardware environment to avoid errors caused by the hardware. The data set is diversified to cover different environmental and motion settings.