# A Practical Deep Learning Architecture for Large-Area Solid Wastes Monitoring Based on UAV Imagery
## 5 Conclusions  
Various semantic segmentation methods have been applied in different research fields by many scholars, but most of them focus on conventional segmentation tasks such as street scenes, farmlands, and buildings. An immediate application to large-area solid waste detection in ultra-high-resolution drone images results in a large number of missed and false detections. In this study, we combined unmanned aerial vehicle remote sensing and deep learning analysis technology to conduct research in 450 km\({}^{2}\) of ultra-high-resolution UAV images, built a dataset of 2600 images, and solved the problem of the lack of high-resolution semantic segmentation datasets for solid waste.  
We then constructed a dual-branch semantic segmentation model that improved the ability to distinguish solid waste from the environment and the ability to segment complex edges. The global attention mechanism was introduced in the contextual feature branch to enhance the discrimination of foreground and background, and the deformable separable convolution was introduced in the spatial feature branch to adapt to complex edges and extract spatial detail information. Finally, we obtained an F1 score of 88.875%, an overall accuracy of 94.390%, a recall rate of 88.626%, and a precision rate of 89.125% on the dataset using our proposed method, which is superior to other 13 state-of-the-art semantic methods. Finally, the trained model was applied to segment the entire regional image, obtaining a distribution map of the entire region, which was successfully applied in large-area solid waste monitoring and governance. In urban solid waste monitoring, this method can save labor costs, facilitate quick treatment, and play a positive role in improving the overall living environment.  
This study differs from solid waste recycling scenarios [21, 24], as it primarily targets the monitoring of exposed solid waste over extensive regions. In comparison to approaches target detection [25, 26], image classification [27, 28, 29], and weakly supervised segmentation [30, 31], this study achieves pixel-level recognition outcomes for solid waste. However, there are certain limitations. With regard to the sample library, the data captured by the visible light band of the unmanned aerial vehicle (UAV) is limited to monitoring exposed solid waste on the ground surface, and is unable to detect waste concealed by vegetation or buried beneath the surface. To address this, there is a potential for introducing thermal infrared data in the future to identify such waste based on temperature changes resulting from the decomposition of garbage. Regarding the model, there is a need to enhance the performance of mixed waste detection, particularly in cases where the mixed waste is dispersed rather than consolidated. Furthermore, the dual-branch structure, while capable of capturing more complementary information, results in an increase in parameter size, thereby escalating the costs associated with training and inference. Therefore, further optimization to improve efficiency is necessary.  
Figure 11: Governance results.  
**Author Contributions:** Y.L.: Methodology, writing, and funding acquisition. X.Z.: Analysis and coding. B.Z.: Investigation. W.N.: Data curation. P.G.: Visualization. J.L.: Review and Editing. K.W.: software. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was financially supported by the Science and Technology Program of Zhejiang Province (Grant No. 2022C35070).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. Data available on request due to privacy.  
**Conflicts of Interest:** The authors declare no conflicts of interest.