# LM-DeeplabV3+: A Lightweight Image Segmentation Algorithm Based on Multi-Scale Feature Interaction
## 1 Introduction  
The continuous development of deep learning and street-view images provides new perspectives for city feature recognition. The element extraction of street-view environment can help us to better understand the city environment and its potential features and, combining street-view data and multi-dimensional feature recognition technology, provides refined technical support for city design [1]. With the development of deep learning and computer vision, the technology of extracting elements from street scenes based on semantic segmentation algorithms is becoming more mature. Street-view images have the advantages of wide coverage, providing street-level landscape information, and low data collection costs, providing a large sample data source and new research ideas for city environmental assessment research [2]. Street-view images contain a large amount of visual information about city space, including static city architectural environments and dynamic pedestrians or vehicles on the streets, providing new perspectives and dimensions for city spatial environment analysis [3]. Researchers have extracted elements from street-view images and conducted some research related to city perception and safety [4; 5; 6]. In recent years, computer vision and deep learning have brought many new opportunities to the field of geographic space [7]. So far, many semantic segmentation algorithms have been widely applied in different fields. From the perspective of model structure, semantic segmentation algorithms mainly have two categories: classification based on information fusion and classification based on encoder-decoder architecture [8]. The method based on information fusion improves the utilization of the model by increasing the number of layersin the network [9]. Representative algorithms include the fully convolutional network (FCN) algorithm [10] and a series of improved algorithms [11], such as FCN-32S, FCN-16S, and FCN-8S. The method based on encoding and decoding [12] improves the accuracy of the network by using different backbone network forms and pyramid pooling modules. Representative algorithms include the pyramid scene parsing network (PSPNet) [13] and DeepLabV3+ [14]. With the emergence of more and more image semantic segmentation algorithms, the challenges to computer computing resources and hardware memory requirements are gradually increasing. The Deeplab [15] series is a series of semantic segmentation algorithms developed by the Google team based on FCN. From 2014 to 2018, the Deeplab series released four versions, namely, v1, v2, v3, and v3+. The most influential change in DeepLabV3+ is the use of a new backbone network, Xception [16], and the addition of an upsampling decoder module modeled after U-Net [17]. The original DeepLabV3 is used as an encoder, and the addition of a decoder results in a new model with better boundary segmentation performance. DeepLabV3+ also integrates the spatial pyramid pooling (SPP) [18] and encoder-decoder into one. DeepLabV3+ refers to a very common feature fusion strategy in object detection, which preserves a considerable amount of shallow information in the network. At the same time, it replaces depthwise separable convolutions with dilated depthwise separable convolutions and proposes Atrous spatial pyramid pooling (ASPP), which can achieve a faster speed while ensuring effectiveness. However, the DeeplabV3+ algorithm has high computational complexity and high memory consumption, making it difficult to deploy in platforms with limited computing power. Moreover, DeeplabV3+ cannot fully utilize multi-scale information when extracting image feature information, which can easily lead to the loss of information detail and affect segmentation accuracy. For some application scenarios that require real-time feedback, such as autonomous driving or real-time monitoring, the limitation of computing resources makes it difficult to complete segmentation tasks in a timely manner.