# A Practical Deep Learning Architecture for Large-Area Solid Wastes Monitoring Based on UAV Imagery
## 1 Introduction  
With the continuous increase in global population and the accelerated process of urbanization, the amount of urban solid waste is also increasing [1, 2]. Managing and handling these solid wastes has become a global challenge [3]. Due to the high cost of waste disposal and the drive for illegal profits, phenomena such as unauthorized dumping and littering of solid waste, as well as the establishment of abandoned disposal sites, are common [4, 5]. Untreated solid waste, after being infiltrated by rainwater and undergoing decomposition, results in leachate and harmful chemicals entering the soil through surface runoff, which disrupts soil structure and causes pollution to nearby rivers and groundwater, leading to serious ecological impacts [6, 7]. Therefore, it is essential to regulate and govern these illegal solid waste materials, as this work holds significant importance for the construction of urban ecological civilization and sustainable development. To conduct large-area solid-waste management, accurate acquisition of the distribution locations and rugged edge information is highly required, so as to provide policy support for estimating the workload and generating specific strategies for their treatment. The previous approaches for monitoring solid waste dumps relied on manual inspections and video surveillance, both of which have many drawbacks [8]. These approacheshave limited detection range and cannot comprehensively cover monitoring areas, especially in remote areas such as suburbs, mountains, riverbanks, and road edges [9; 10]. Inasmuch, many scholars have applied remote sensing technology to relevant research, such as [PERSON] et al. [11], who used thermal infrared remote sensing technology to measure land surface temperature (LST) and outline the most likely dumping areas within landfill sites. However, the sensitivity of temperature difference detection is low, and the LST changes in small garbage heaps are not significant. Other scholars have considered feature extraction approaches, such as [PERSON] et al. [12], who proposed an edge-based intelligent mobile service system for detecting illegal dumping of solid waste. However, the accuracy of such algorithms depends on manually designed features, and although they perform well on some samples, they cannot be applied to large-area automated identification due to the complex and diverse structures and textures of garbage heaps. In addition, satellite imagery as a data source is prone to cloud cover, and the limitations of revisit periods restrict the application of related methods [13]. This paper utilizes unmanned aerial vehicle (UAV) to gather data, which have the characteristics of high resolution, wide monitoring range, and real-time capabilities [14; 15]. Moreover, UAV can perform monitoring in hard-to-reach areas, providing a solid data foundation for extracting characteristic information of solid waste and identifying targets in various garbage scenarios [16; 17]. In recent years, deep learning technology has gradually matured and is widely used in various complex scenarios due to its powerful feature learning and representation capabilities compared to conventional methods [18; 19]. Some scholars have introduced it into the research field of garbage classification [20; 21; 22; 23; 24]. Current research mostly regards deep learning as three tasks: target detection [25; 26], image classification [27; 28; 29], and weakly supervised segmentation [30; 31]. For example, [PERSON] et al. [32] proposed a street garbage detection algorithm based on remote sensing satellite SIFT feature image registration and fast regional convolutional neural networks. [PERSON] et al. [29] proposed a waste classification method based on a multi-layer hybrid convolutional neural network, changing the number of network modules and channels to improve the model's performance. [PERSON] et al.