# Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5
## 4 Results
### Test Results and Analysis
#### 4.2.4 Pascal Voc 2012 Dataset Validation  
The public dataset selected for this experiment is Pascal voc 2012, with 20 category types. Its tag format is xml, but YOLOv5 needs txt format file, so we need to convert the xml format tag to txt format first. Then the 17,125 images were divided into training and validation sets, with 13,637 images in the training set and 3488 images in the validation set. The training conditions are consistent with those described above, and their results on the validation set are shown in Figure 14. In the mAP_0.5 and mAP_0.5:0.95 metrics, the improved YOLOv5 is 0.047 and 0.05 higher than the original model, respectively. of true positives was 1548, the number of false positives was 48, and the number of false negatives was 64. False positive image types tend to have similarities between the target to be detected and the background, which may be mainly due to the following three reasons. Firstly, the content of the image. When training the model, in order to improve the generalization of the model, data enhancement is generally used to simulate complex situations such as different illumination and different angles in the image. This process may make some images too bright or too dark. After these images have been extracted by the model, if they are similar to some background features extracted by the model, the model will detect the background as an object. Secondly, it is necessary to consider the scope of the bounding box. In the process of model training, it is necessary to provide the position of the target in the picture, that is, the enclosing rectangle. However, the general target to be detected is not a rectangle, and there will be some background contents inside the label, which could also be responsible for the false positive. Thirdly, when the drone is flying at high altitudes, the characteristics of wild animals are proportionally relatively close, which could also be a reason for false detection. A test video can be found at reference [49]. The overall effect is ok, but there are some shortcomings. Some elephants are sometimes misidentified as giraffes. We guess that the reason for this is that during the training of the model, part of the data enhancement darkened the picture to which the giraffe belongs, which would make its features similar to those of the elephant in the video, thus leading to misidentification. The problems of the missed detection of small targets and the difficulty of detecting occluded objects in videos still need further research. One thing to note is that the lightness of the model facilitates deployment. The speed of wildlife detection is also extremely important for drones. The recognition speed in [PERSON]'s [15] paper was 2-3 fps, and was not able to meet the requirements of real-time detection by UAV. The detection speed of the model proposed in this paper is 26 fps, 12 times of its detection speed, which meets the requirements of real-time detection using UAVs. Although the YOLOV4 [6] model has high detection accuracy, its model is too large, which is not conducive to the deployment of embedded devices. The model YOLOv4-uw proposed by [PERSON] et al. [14]. has reached a detection speed of 43 fps, but its accuracy is relatively low, which can easily cause the phenomenon of missed or false detection. The application of the Tiny-YOLOv3 model by [PERSON] et al. [13]. meets the requirements of lightweight deployment, but its observation of animals mainly from the ground perspective does not meet the needs of this paper applied to UAVs. In conclusion, the target detection model proposed in this paper takes into account the accuracy and real-time requirements. The accuracy of detection is ensured while real-time detection is performed. At the same time, this paper solves to a certain extent the problem that the change of target occupancy ratio makes detection difficult.