# Real-Time UAV Trash Monitoring System
## 4 Experimental Results  
We deployed the YOLOv4-Tiny-3l model [38] to the NVIDIA-embedded system Xavier NX on the UAV for the UAV trash monitoring system and tested it with three scenarios (NTOU campus, Badouzi fishing port, and Wanghai Xiang beach). The Mongo database was used in this study. The HAIDA dataset, which was collected in the NTOU campus, was applied to train the object detector (YOLO model). This dataset had 1319 pictures of trash, included two classes (3904 garbage objects and 2571 bottle objects) and 456 negative samples that were collected by the UAV. In this study, the dataset was split into three parts, a training set for model fitting, a validation set for hyper-parameter tuning and model selection, and a testing set for model evaluation. All models were trained on NVIDIA GeForce RTX-2080 Ti GPU. After training, the YOLO model was implemented into the onboard computer Xavier NX. The YOLO model could process 22 FPS (frames per second) and identify trash objects with more than 70% AP50 (Test). Trash objects could be detected in real time. The flight time of the UAV was 21 min for each mission. The cruising speed of the UAV was 2 m/s. The UAV could fly 1 km in one mission, and the path was planned before the mission. The planned path was set in the flight controller of the UAV. Several waypoints were preset along the path. The UAV flew through all waypoints and obtained images of the coastal area. At the same time, the UAV sent a real-time video stream to the base station via mobile communication. In the video, one image frame covered a 10 m \(\times\) 10 m area. Thus, the trash monitoring system processed one coastal picture and transmitted this still image to the base station every 5 s.  
First, regular-shaped trash (bottle) detection was tested in the NTOU campus, as shown in Figure 13. As shown in Figure 13b, the confidence scores of small object detections were high (98.27%, 64.65%, and 91.86%). Figure 13: _Cont._  
In the Badouzi fishing port, the shape and the texture of the garbage vary due to natural erosion, thus decreasing the confidence scores of garbage detection (42.97%, 9.97%, 41.87%, 5.29%, and 8.49%). Thus, for the HAIDA dataset, there was a large bias to the trash of the Badouzi fishing port. The best solution for this problem was to collect more data for training. Figure 14a shows the UAV in the Badouzi fishing port, and Figure 14b,c show the detection of large, medium, and small objects. Figure 15 shows the UAV and trash information indicated on the proposed trash monitoring system. Figure 13: The result of the unmanned aerial vehicle (UAV) trash monitoring system in NTOU campus. (**a**) An area with the UAV and trash. (**b**) Video streaming server: detection result sent by the UAV. (**c**) UAV map: real-time monitoring by the UAV and the detection result. Figure 14: The result of the unmanned aerial vehicle (UAV) trash detection at the Badouzi fishing port. (**a**) The UAV in Badouzi fishing port at 10 m height. (**b**) Video streaming server: the detection result (a small area) in the Badouzi fishing port. (**c**) Video streaming server: the detection result (a large area) in the Badouzi fishing port. In the third case, there was more plastic debris on the Wanghai Xiang beach. The system easily confused both humans and the trash, and thus, gave false positive (FP) detection (whether it was a stone, garbage, or some plastic debris), as shown in Figure 16b-d.  
Figure 15: Trash and the unmanned aerial vehicle (UAV) information on the trash monitoring system. (a) A UAV map to monitor the UAV waypoints and the status in the Badouzi fishing port. (b) A UAV map to monitor the trash detection and the trash polluted area in the Badouzi fishing port. Figure 16: _Cont._  
After real-world testing for the Badouzi fishing port, we analyzed the trash data with the Mongo database. Figure 17 shows the degree of trash pollution map (based on trash polluted area) via our website. The red area means there was more trash.