VEHICLE MASSIVE IMAGE DATA FILTERING AND USELESS IMAGE REUSE BASED ON FARMLAND BACKGROUND ANALYSIS

The real-time images captured by agricultural machinery on-board monitoring equipment have complex backgrounds and different shooting angles. Especially for straw monitoring tasks, the utilization rate of images is relatively low. This paper presents a novel image classification and effective region segmentation method for straw returning in agriculture, leveraging semantic segmentation to enhance the efficiency of agricultural data analysis. The study addresses the challenges of manual straw cover analysis by proposing an automated approach to select images that meet monitoring standards. The methodology employs an encoder-decoder structure model, enriched with residual units, multi-scale convolution, and attention mechanisms. This model classifies images by calculating the pixel proportions of various scene categories and segments farmland areas to be inspected by incorporating distance information. The model's design is tailored to handle the complex and variable natural environments typical of vehicular monitoring scenarios, where semantic object boundaries can be fuzzy. The experimental results demonstrate that the proposed method achieves an overall sample classification accuracy of 93% for straw returning image classification and an 85.37% accuracy in dividing areas to be inspected. The method outperforms several mainstream semantic segmentation models, providing a more accurate and efficient means of processing agricultural monitoring images. The integration of distance information proves particularly beneficial in distinguishing the farmland areas under inspection, leading to clearer segmentation and more reliable data for agricultural decision-making. In conclusion, the study contributes to the field of agricultural intelligence by offering a robust method for image analysis that can be applied to optimize the use of straw return monitoring data.