# Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions
## 3 Experiment and Results
### Results  
The original YOLOv5 model and YOLOv5_Ours model were trained at 100 epochs and with the 3360 images: training images, validation images, and testing images. As a result of training all models, the average time spent training was about 2 h per model. The model that took the most time was the original YOLOv5 model, which took 2 h and 10 min. The object detection comparison results of the two models (the original YOLOv5 model and the YOLOv5_Ours model) are shown in Table 2 and Figure 11. Additionally, this table shows the Precision, Recall, F-1 score, and mAP of the original YOLOv5 model and YOLOv5_Ours. We compared based on the best of the 100 epochs result values. In order to objectively evaluate the performance of the models, the values of mAP (Mean average precision) were compared. The mAP value of the original YOLOv5 model is 94.6%, and YOLOv5_Ours is 95.5%. Overall, it may be seen that the YOLOv5_Ours model has higher than the original YOLOv5 model.  
As a result of the training and validation process, we found that the YOLOv5_Ours model was the best. Thus, the final prediction was made based on the weight obtained from the trained YOLOv5_Ours model, which was considered to have the best performance. The left part of Figure 12 shows the graphs of the metrics curves as training progresses. It is proved the detection accuracy of the YOLOv5_Ours model [43]. After evaluation, the YOLOv5_Ours model had a validation precision score of 90.7%, recall score of 87.4%, as well as F1-score of 88.8%, and mAP score is 95.5%. This result confirms the effectiveness of our approach in predicting experiment performed in several environments correctly.  
The first three columns are the YOLOv5_Ours model loss components, box loss, objectness loss, and classification loss, train the leftmost row and validation second row [44]. The box loss, objectness loss, and classification loss are indicators of how well an algorithm predicts an object [45]. These results mean that the three classes: person, car, and notice, which we use for detection, are accurately recognized during the training process.  
Precision-Recall curve is a method of evaluating the performance of an object detector due to a change in the threshold value for the confidence level. The confidence level is a value that tells user how confident the algorithm is about the detection. In other words, the closer the number is to 1, the more confident the model is in detecting the target object. The right part of Figure 12 is the Precision-Recall curve graph of the YOLOv5_Ours model. It can be seen that the value of person is 97.3%, which is quite high.  
The results are shown in Figure 13 by experimental conditions: Clear, Cloudy, Rainy, Snowy day, Evening, Night, Low altitude, and High altitude. For clear day and evening, object detection showed high accuracy above the value of about 87.0%. Rainy day is relatively low, about 57.0%, but overall, object detection is excellent.  
It can be seen from Table 3 that the object detection results of the YOLOv5_Ours model. Among the detected objects, the value for a person was the highest. The person detection was calculated as 97.1% for Precision, 84.3% for Recall, 90.2% for F1-Score, and finally 97.3% for mAP. This means that the person detection rate is quite high.  
The function loss difference between the two models results in a large gap at the beginning of the training. Therefore, the experiment was conducted by setting the epoch to 100.  
It can be seen that YOLOv5 function loss occurs rapidly at the beginning of training. On the other hand, YOLOv5_Ours decreased function loss slowly. The gap appears to be narrowing until the epoch reaches 60. After that, the function loss of the two models: Original YOLOv5 and YOLOv5_Ours, is a little different. Figure 14 shows a graph comparing  
Figure 12: Graph of result values for YOLOv5_Ours model. (**Left** figure is changes in key indicators according to the epochs of training; **Right** figure is Precision–Recall curve.).