# Research on a UAV-View Object-Detection Method Based on YOLOv7-Tiny
## 4 Experiments and Results
### Experiment
#### 4.4.3 Object-Detection Results  
To accurately evaluate the performance of the improved version of the YOLOv7-tiny algorithm, we randomly selected multiple challenging drone aerial views from the VisDrone2019 dataset's test set for object-detection experiments. We compared the results with the original YOLOv7-tiny algorithm. The algorithm's performance was evaluated under various scenario conditions, including dense objects, occluded objects, low-light environments, fast-moving objects, tiny objects, and complex backgrounds. Dense objects (Figure 10): This figure compares object-detection results on a traffic-congested urban street where vehicles and pedestrians are densely distributed. The improved YOLOv7-tiny algorithm demonstrates excellent recognition capability in such an environment. Even when objects are very close or partially overlapping, the algorithm can accurately distinguish and label various vehicles and pedestrians. Ocluded objects (Figure 11): In this scene, many objects are partially obscured by other objects. The improved algorithm effectively identified most of partially occluded vehicles and pedestrians, demonstrating its precise recognition ability under object occlusion. Low-light environments (Figure 12): This figure compares object detection in nighttime low-light conditions. The improved algorithm effectively detects pedestrians and bicycles, showcasing enhanced performance and robustness in poor lighting scenarios. Fast-moving objects (Figure 13): This figure compares object-detection results in a high-speed urban traffic scenario. The improved algorithm accurately identifies fast-moving vehicles, demonstrating enhanced detection performance under dynamic conditions. Figure 11: Comparison of detection results in occluded object views within the VisDrone2019 dataset, with the algorithm from this study on the left and the YOLOv7-tiny algorithm on the right. Figure 10: Comparison of detection results on dense object views in the VisDrone2019 dataset, with the left showing the algorithm results from this study, and the right showing the YOLOv7-tiny algorithm. Tiny objects (Figure 14): This figure compares object-detection results for a cityscape taken at a distance, which includes several objects of extremely small size. The improved algorithm successfully identifies these tiny vehicles and pedestrians, demonstrating its significant improvement in detecting small-sized objects at long distances. Complex background (Figure 15): This image captures an urban road with a complex background, including various dynamic and static distracting elements. The improved version of the algorithm accurately identifies the objects in this complex background, significantly reducing interference from background noise. In addition, this study uses heatmaps to illustrate the model's attention distribution further. As shown in Figure 16, the brighter areas on the heatmap represent regions of higher focus. The algorithm proposed in this study demonstrates a global focus on objects, including small and distant objects, showcasing improved detection performance compared to the original model. This further validates the effectiveness of the proposed improvements. Figure 14: Comparison of detection results for tiny objects in the VisDrone2019 dataset, with the algorithm from this study on the left and the YOLOv7-tiny algorithm on the right. Figure 13: Comparison of detection results in fast-moving objects views within the VisDrone2019 dataset, with the algorithm from this study on the left and the YOLOv7-tiny algorithm on the right. Figure 15: Comparison of detection results in complex background views within the VisDrone dataset, with the algorithm from this study on the left and the YOLOv7-tiny algorithm on the right. Overall, the algorithm proposed in this study significantly outperforms the original YOLOv7-tiny, particularly in addressing challenges such as small object recognition, occlusion handling, and object detection in complex backgrounds.