Vehicle Density Detection Using Dynamic Contour UNET Segmentation - K. Mohanapriya (International Journal of Intelligent Systems and Applications in Engineering, 2024)
There needs to be an improvement in traffic management systems since the fast expansion of cities has increased the number of vehicles on the road. Improving the precision and consistency of vehicle density and vehicle type recognition is the primary goal of this study, which employs state-of-the-art data mining approaches in conjunction with picture denoising and segmentation methods. The suggested technique integrates dynamic contour UNET segmentation for accurate object detection with Non-Local Wavelet Wiener Denoising for picture improvement. At the outset, we denoise the photos using the cutting-edge Non-Local Wavelet Wiener Denoising method, which manages to extract useful information from the images while simultaneously eliminating noise. The input photos are improved in quality by this technique, laying a clean slate for further analysis. To isolate and identify specific automobiles in the photos, the next phase uses dynamic contour UNET segmentation for picture segmentation. Precise vehicle boundary delineation is guaranteed by the UNET architecture, which can record both global and local characteristics. With the dynamic contour mechanism, segmentation becomes more adaptable, leading to strong performance in a wide range of traffic circumstances and environmental conditions.