# SE-Lightweight YOLO: Higher Accuracy in YOLO Detection for Vehicle Inspection
## 5 Experiment
### Analysis of the Experiment Outcome  
The confusion matrix obtained from the experiment is shown above, where the columns represent the predicted categories, and the rows represent the actual categories. The values on the diagonal represent the proportion of correct predictions; the off-diagonal elements are the fraction of incorrect predictions. Higher values on the diagonal of the confusion matrix indicate many correct predictions. Especially in cases where the amount of data across categories is unbalanced, the confusion matrix provides a visual indication of the accuracy of the classification model corresponding to each category. The above Figure 13 shows that the model maintains a high level of recognition accuracy for each category. Figure 12: Comparison of noise identification with and without noise. Figure 13: Confusion matrix. From the comparison in the above Figure 14, it can be seen that the improved model has improved the recognition accuracy in all categories compared to the original model; this can be seen from the detection accuracy of vehicles with a lot of occlusion in the second picture in each line. However, the improved model could be more precise for the recognition accuracy of the more obvious targets. Nevertheless, for targets with overlap (based on the farthest vehicle in the picture), the recognition accuracy is still improved by 5%, from 89% to 94%, which shows that the improved overlapping target recognition accuracy model outperforms the traditional model. From the above results in Figure 15, it can be seen that the model training has achieved good results in the 85 th epoch. The model shows convergence properties and good stability after 85 rounds. The F1 curve represents the relationship between the score and the confidence threshold (x-axis). The F1-score is a measure of categorization and is the harmonic mean of  
Figure 14: Detection of YOLOv7 and SE-Lightweight YOLO in the dataset. Figure 15: Demonstration of SE-Lightweight YOLO training process. precision and recall between 0 and 1. The F1-score is the average of the precision and recall scores, whereby the larger, the better. In general, when the confidence threshold (the probability threshold that the sample is judged to be a specific category) is low, many samples with low confidence are considered to be accurate, with high recall and low precision; when the confidence threshold is high, samples with high confidence can only be considered to be accurate, and the category is detected more accurately, i.e., the precision rate is more significant (only if the confidence is enormous is it judged to be a specific category). Hence, the before and after F1-scores at both ends are relatively small. It can be seen in Figure 16 that the F1 curve is very \"spacious\", and the top is close to 1, indicating that there is an extensive range of confidence thresholds that perform well on the training dataset (both for complete and accurate detection). Combined with the above figure, it can be seen that the model works best for trucks and has a weaker accuracy in recognizing people than the other categories. The meaning of the P curve is the accuracy of each category recognition when the determination probability exceeds the confidence threshold. When the confidence level is higher, the category detection is more accurate, but there is a risk of missing some actual samples with lower determination probability. From the below Figure 17, we can obtain the same conclusion as the F1 curve in Figure 16, i.e., the model has the best recognition effect on trucks and a relatively weak recognition effect on people. Recall values range from 0 to 1 and are usually expressed as percentages. A high recall value indicates that the model effectively finds most or all valid targets, while a low recall value indicates that the model misses many proper targets. In YOLO, recall curves are often used to visualize the model's recall value at different thresholds. YOLO models use a confidence threshold to determine whether a detection is valid.