# Mobile Robot Tracking with Deep Learning Models under the Specific Environments
## 4 Results and Discussion  
In this section, more numerical experimental results are presented to demonstrate the anti-occlusion performance of the deep learning models with the generated data set. YOLOv5, Faster RCNN and SSD are chosen as the benchmarks. Labeling was used to label images in the data set. The target object is the robot vehicle. After labeling the images, the model is fine-turned according to the training data set. Training time, F1 Score, Precision and Recall are adopted as evaluation metrics. Score_threshold is the threshold of confidence. Prediction results greater than this value are retained. Figure 4: Duplicate detection in Faster RCNN. The training times of the three models are also tested. The test data set is created using the images and videos of the target robot at different angles indoors and outdoors. Initially, the random erasure method is not applied. The three models all provide perfect results with 100% accuracy and recall. When more challenging situations are simulated, i.e., when the random erasure method is adopted, the PR curves of the three models for testing data set one and testing data set two are shown in Figures 5 and 6, respectively. As shown in Figure 5, the Faster RCNN model has a PR curve most close to the upper right corner. The YOLOv5 has the best performance for test data two, as shown in Figure 6. The values of precision and recall are also different under different confidence levels. The data in this project use the specified value when the confidence is set to 0.5. We need to use other testing parameters to further judge which model has the best performance. The configuration files of the models are also modified to train the data set. Accuracy quantifies the correlation of detection targets. Recall refers to the ratio of detected targets to all existing targets. The F1 score is an index used to measure the accuracy of the binary classification model in statistics. It takes into account the accuracy and recall of the classification model. The training time of these models is also tested. As shown in Table 2: Testing Results for testing data set one, Faster RCNN and SSD have a comparable performance of 0.87 and 0.89 respectively in the F1 score, which is much better than 0.54 with the YOLOv5 model. The SSD model achieves the highest F1 score. The precision of Faster RCNN, SSD and YOLOv5 are 93.38%, 97.72% and 97.69%. The SSD model achieves the highest precision value. The recall values of Faster RCNN, SSD and YOLOv5 are 77.62%, 81.16% and 63.66%, respectively. Again, the SSD model achieves the highest value. As shown in Table 3: Testing Results for testing data set two, the F1 score for three models are 0.89, 0.81 and 0.98. The Faster RCNN model achieves the highest F1 score. The precision of Faster RCNN, SSD and YOLOv5 is 91.55%, 76.96% and 98.18%. The YOLOv5 model achieves the highest precision value. YOLOv5 also achieves a high score of 98.18% for the recall, which is better than the recall scores of 87.54% and 81.99% for Faster RCNN and SSD, respectively. Meanwhile, the Faster RCNN model spends about 48.25 h on training. The SSD model spends the least training time, about 15.16 h. The YOLOv5 model spends about 20.25 h on training. The detection speed of Faster RCNN is about 1.6 frames per second. For SSD and YOLOv5 models, the detection speeds are about 6.6 and 3 frames per second, respectively. According to the values in Table 2: Testing Results for testing data set one and Table 3: Testing Results for testing data set two, it is difficult to decide which model has the best overall performance. In order to further judge the performance of these models, we defined a variable called \(P\) which stands for the performance of the model in the designed experiments. The F1 score is regarded as an index to measure the accuracy of the binary classification model in statistics. It takes into account both the accuracy and recall of the classification model. F1 score can be regarded as the weighted average of model accuracy and recall rate.