A Low-Altitude Remote Sensing Inspection Method on Rural Living Environments Based on a Modified YOLOv5s-ViT

Abstract

The governance of rural living environments is one of the important tasks in the implementation of a rural revitalization strategy. At present, the illegal behaviors of random construction and random storage in public spaces have seriously affected the effectiveness of the governance of rural living environments. The current supervision on such problems mainly relies on manual inspection. Due to the large number and wide distribution of rural areas to be inspected, this method is limited by obvious disadvantages, such as low detection efficiency, long-time spending, and huge consumption of human resources, so it is difficult to meet the requirements of efficient and accurate inspection. In response to the difficulties encountered, a low-altitude remote sensing inspection method on rural living environments was proposed based on a modified YOLOv5s-ViT (YOLOv5s-Vision Transformer) in this paper. First, the BottleNeck structure was modified to enhance the multi-scale feature capture capability of the model. Then, the SimAM attention mechanism module was embedded to intensify the model’s attention to key features without increasing the number of parameters. Finally, the Vision Transformer component was incorporated to improve the model’s ability to perceive global features in the image. The testing results of the established model showed that, compared with the original YOLOv5 network, the Precision, Recall, and mAP of the modified YOLOv5s-ViT model improved by 2.2%, 11.5%, and 6.5%, respectively; the total number of parameters was reduced by 68.4%; and the computation volume was reduced by 83.3%. Relative to other mainstream detection models, YOLOv5s-ViT achieved a good balance between detection performance and model complexity. This study provides new ideas for improving the digital capability of the governance of rural living environments.

Sugarcane stem node identification algorithm based on improved YOLOv5

“…Recently, deep learning models represented by convolutional neural networks have developed rapidly. As very effective classification and recognition models, they have attracted considerable attention worldwide, been widely used [11][12][13][14][15], and achieved good results in the agricultural field. Examples include fruit identification [16,17], crop diseases and pests identification [18,19], animal behavior detection [20,21], etc.…”

Advancing Rural Building Extraction via Diverse Dataset Construction and Model Innovation with Attention and Context Learning

“…The convolutional feature maps are generated as the output from the Block5 section of the encoder, and these maps serve as the input for the atrous spatial pyramid pooling. The ASPP module contains 4 parallel branches using dilated convolutions with different dilation rates (1,6,12,18) to obtain feature information at different scales. The 4 groups of feature maps after 1 × 1 convolution are concatenated to obtain feature expressions sensitive to multiscale features.…”

“…Therefore, extracting rural buildings is more challenging, and related research is relatively scarce. In addition, the high cloud cover in rural areas increases the extraction difficulty [6]. Accurately identifying rural building roof types is of great importance for rural revitalization, environmental planning, energy assessment and disaster management [7][8][9][10].…”

“…Difficulties exist in recognizing complex and diverse rural building types. Additionally, the high cloud cover in rural areas affects satellite image quality [6]. To solve this problem, lowaltitude remote sensing technologies have advantages, such as low cost and high-quality data acquisition, which can effectively improve the accuracy of rural building recognition.…”

Study of a Machine Vision Approach to Leak Monitoring of a Marine System

“…As the best algorithm of the YOLO family, YOLv5 has a slow detection speed, and there are problems involving the false detection of leaks in large objects, which cannot be detected well. Therefore, research scholars have started to study the improved YOLOv5 algorithm, and the main improvement methods are Transformers [20][21][22][23], attention mechanisms [24][25][26][27], etc.…”