# Construction of Three-Dimensional Semantic Maps of Unstructured Lawn Scenes Based on Deep Learning
## 2 Related Work  
### Technology for the Semantic Segmentation Recognition of Images  
For gardening pruning robots, detecting a viable working area is a prerequisite for autonomous navigation. In the realm of semantic detection, scholars worldwide have proposed a plethora of detection algorithms for image semantic segmentation in recent decades, such as PSPNet, U-Net, and HRNet. Although research on the semantic segmentation of lawn scenes is relatively scarce, semantic segmentation networks have found widespread application in other areas.  
In terms of reducing the complexity of the backbone network, [PERSON] et al. proposed a method for tea bud segmentation and picking point localization based on a lightweight convolutional neural network, Multiclass DeepLabV3+ MobileNetV2 (MC-DM). By optimizing MobileNetV2 and introducing a densely connected spatial pyramid pooling module, they achieved high-precision tea bud segmentation [10]. [PERSON] et al., aiming to achieve the automatic and effective extraction of stand-alone tree elements in images, proposed a lightweight network segmentation model, SENet and MobileNet embedded in DeepLabV3+ (SEMD). By choosing DeepLabV3+ as a base framework, integrating MobileNet to reduce complexity, and introducing SENet to capture feature information, they managed to achieve the high-precision segmentation of stand-alone tree images against complex backgrounds [11].  
In terms of enhancing model accuracy, [PERSON] et al. improved the DeepLabV3+ network model by embedding the CBAM module and utilizing SENet to optimize the decoding part, significantly enhancing the accuracy and localization precision of semantic segmentation for parts of Tilapia, effectively addressing the issues of target edge and small object segmentation [12]. [PERSON] et al., addressing the issue of the inaccurate segmentation of strawberries at different maturity stages, proposed a method based on an improved DeepLabV3+ model. By introducing an attention mechanism to adjust the weights of neural network feature channels, they significantly enhanced the feature information of strawberry images, markedly improving segmentation precision [13]. [PERSON] et al. proposed a Lateral Hierarchically Refining Network (LHRNet) for the precise detection of salient objects, effectively integrating multi-level features and merging coarse semantics with fine details to produce more reliable predictions [14].  
Regarding fusion with other algorithms, [PERSON] et al. proposed a non-contact measurement method for concrete plane structure cracks based on binocular vision, using an improved DeepLabV3+ model for crack area segmentation combined with SIFT feature-point-matching principles to calculate crack width, proving that this method solves the problem of the rapid and convenient measurement of concrete crack width in outdoor environments [15].  
It is evident that semantic segmentation has provided rich solutions for addressing various issues, hence the motivation to apply this approach to constructing lawn environments with the intention to achieve effective lawn semantic segmentation results.